Hi, I'm using pyarrow to read parquet data from s3 and I'd like to be able
to parse the schema and convert it to a format suitable for running an
mLeap serialized model outside of Spark.
This requires parsing the schema.
If I had a Pyspark dataframe, I could do this:
test_df = spark.read.parquet(test_data_path)
schema = [ { "name" : field.simpleString().split(":")[0], "type" :
field.simpleString().split(":")[1] }
for field in test_df.schema ]
How can I achieve the same if I read the data using pyarrow instead ?
Also, for the Spark dataframe I can obtain the rows in a suitable format
for model evaluation by doing the following:
rows = [[field for field in row] for row in test_df.collect()]
How can I achieve a similar thing using pyarrow ?
Thanks in advance for your help.
Femi Anthony
--
Card Machine Learning (ML) Team, Capital One
________________________________________________________
The information contained in this e-mail is confidential and/or proprietary to
Capital One and/or its affiliates and may only be used solely in performance of
work or services for Capital One. The information transmitted herewith is
intended only for use by the individual or entity to which it is addressed. If
the reader of this message is not the intended recipient, you are hereby
notified that any review, retransmission, dissemination, distribution, copying
or other use of, or taking of any action in reliance upon this information is
strictly prohibited. If you have received this communication in error, please
contact the sender and delete the material from your computer.
