I was able to get it to work using:
[{'name': t.schema[i].name, 'type': str(t.schema[i].*physical_type*)}
for i in range(len(t.schema))]
Thanks a mill.
Femi
On Fri, Dec 21, 2018 at 3:29 AM Femi Anthony <[email protected]>
wrote:
> I'll try it out, thanks Wes.
>
> Femi
>
> On Thu, Dec 20, 2018 at 7:51 PM Wes McKinney <[email protected]> wrote:
>
>> Does something like this work?
>>
>> In [12]: import pyarrow.parquet as pq
>>
>> In [13]: t =
>> pq.read_table('../cpp/submodules/parquet-testing/data/alltypes_dictionary.parquet')
>>
>> In [14]: t.schema
>> Out[14]:
>> id: int32
>> bool_col: bool
>> tinyint_col: int32
>> smallint_col: int32
>> int_col: int32
>> bigint_col: int64
>> float_col: float
>> double_col: double
>> date_string_col: binary
>> string_col: binary
>> timestamp_col: timestamp[ns]
>>
>> In [15]: [{'name': t.schema[i].name, 'type': str(t.schema[i].type)}
>> for i in range(len(t.schema))]
>> Out[15]:
>> [{'name': 'id', 'type': 'int32'},
>> {'name': 'bool_col', 'type': 'bool'},
>> {'name': 'tinyint_col', 'type': 'int32'},
>> {'name': 'smallint_col', 'type': 'int32'},
>> {'name': 'int_col', 'type': 'int32'},
>> {'name': 'bigint_col', 'type': 'int64'},
>> {'name': 'float_col', 'type': 'float'},
>> {'name': 'double_col', 'type': 'double'},
>> {'name': 'date_string_col', 'type': 'binary'},
>> {'name': 'string_col', 'type': 'binary'},
>> {'name': 'timestamp_col', 'type': 'timestamp[ns]'}]
>>
>> On Wed, Dec 19, 2018 at 2:16 AM Femi Anthony
>> <[email protected]> wrote:
>> >
>> > 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
>> >
>> > ________________________________
>> >
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>
>
> --
> Card Machine Learning (ML) Team, Capital One
>
--
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
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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
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