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Posted to user@arrow.apache.org by Femi Anthony <ol...@capitalone.com> on 2018/12/19 08:15:50 UTC
Parsing schema of pyarrow.parquet.ParquetDataset object
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.
Re: [External Sender] Re: Parsing schema of pyarrow.parquet.ParquetDataset
object
Posted by Femi Anthony <ol...@capitalone.com>.
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 <ol...@capitalone.com>
wrote:
> I'll try it out, thanks Wes.
>
> Femi
>
> On Thu, Dec 20, 2018 at 7:51 PM Wes McKinney <we...@gmail.com> 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
>> <ol...@capitalone.com> 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
>> >
>> > ________________________________
>> >
>> > 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.
>>
>
>
> --
> 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 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.
Re: [External Sender] Re: Parsing schema of pyarrow.parquet.ParquetDataset
object
Posted by Femi Anthony <ol...@capitalone.com>.
I'll try it out, thanks Wes.
Femi
On Thu, Dec 20, 2018 at 7:51 PM Wes McKinney <we...@gmail.com> 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
> <ol...@capitalone.com> 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
> >
> > ________________________________
> >
> > 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.
>
--
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.
Re: Parsing schema of pyarrow.parquet.ParquetDataset object
Posted by Wes McKinney <we...@gmail.com>.
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
<ol...@capitalone.com> 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
>
> ________________________________
>
> 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.