amol- commented on a change in pull request #10999: URL: https://github.com/apache/arrow/pull/10999#discussion_r699349194
########## File path: docs/source/python/getstarted.rst ########## @@ -0,0 +1,149 @@ +.. Licensed to the Apache Software Foundation (ASF) under one +.. or more contributor license agreements. See the NOTICE file +.. distributed with this work for additional information +.. regarding copyright ownership. The ASF licenses this file +.. to you under the Apache License, Version 2.0 (the +.. "License"); you may not use this file except in compliance +.. with the License. You may obtain a copy of the License at + +.. http://www.apache.org/licenses/LICENSE-2.0 + +.. Unless required by applicable law or agreed to in writing, +.. software distributed under the License is distributed on an +.. "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +.. KIND, either express or implied. See the License for the +.. specific language governing permissions and limitations +.. under the License. + +.. _getstarted: + +Getting Started +=============== + +Arrow manages data in Arrays (:class:`pyarrow.Array`), which can be +grouped in tables (:class:`pyarrow.Table`) to represent columns of data +in tabular data. + +Arrow also exposes supports for various formats to get those tabular +data in and out of disk and networks. Most commonly used formats are +Parquet (:ref:`parquet`) and the IPC format (:ref:`ipc`). + +Creating Arrays and Tables +-------------------------- + +Arrays in Arrow are collections of data of uniform type. That allows +arrow to use the best performing implementation to store the data and +perform computation of it. So each array is meant to have data and +a type + +.. ipython:: python + + import pyarrow as pa + + days = pa.array([1, 12, 17, 23, 28], type=pa.int8()) + +multiple arrays can be combined in tables to form the columns +in tabular data according to a provided schema + +.. ipython:: python + + months = pa.array([1, 3, 5, 7, 1], type=pa.int8()) + years = pa.array([1990, 2000, 1995, 2000, 1995], type=pa.int16()) + + birthdays_table = pa.table([days, months, years], + schema=pa.schema([ + ('days', days.type), + ('months', months.type), + ('years', years.type) + ])) + + birthdays_table + +See :ref:`data` for more details. + +Saving and Loading Tables +------------------------- + +Once you have a tabular data, Arrow provides out of the box +the features to save and restore that data for common formats +like parquet + +.. ipython:: python + + import pyarrow.parquet as pq + + pq.write_table(birthdays_table, 'birthdays.parquet') + +Once you have your data on disk, loading it back is as easy, +and Arrow is heavily optimized for memory and speed so loading +data will be as quick as possible + +.. ipython:: python + + reloaded_birthdays = pq.read_table('birthdays.parquet') + + reloaded_birthdays + +Saving and loading back data in arrow is usually done through +:ref:`parquet`, :ref:`ipc`, :ref:`csv` or :ref:`json` formats. + +Performing Computations +----------------------- + +Arrow ships with a bunch of compute functions that can be applied +to its arrays, so through the compute functions it's possible to apply +transformations to the data + +.. ipython:: python + + import pyarrow.compute as pc + + pc.value_counts(birthdays_table["years"]) + +See :ref:`compute` for a list of available compute functions and +how to use them. + +Working with big data Review comment: :+1: renamed "big" to "large" ########## File path: docs/source/python/getstarted.rst ########## @@ -0,0 +1,149 @@ +.. Licensed to the Apache Software Foundation (ASF) under one +.. or more contributor license agreements. See the NOTICE file +.. distributed with this work for additional information +.. regarding copyright ownership. The ASF licenses this file +.. to you under the Apache License, Version 2.0 (the +.. "License"); you may not use this file except in compliance +.. with the License. You may obtain a copy of the License at + +.. http://www.apache.org/licenses/LICENSE-2.0 + +.. Unless required by applicable law or agreed to in writing, +.. software distributed under the License is distributed on an +.. "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +.. KIND, either express or implied. See the License for the +.. specific language governing permissions and limitations +.. under the License. + +.. _getstarted: + +Getting Started +=============== + +Arrow manages data in Arrays (:class:`pyarrow.Array`), which can be +grouped in tables (:class:`pyarrow.Table`) to represent columns of data +in tabular data. + +Arrow also exposes supports for various formats to get those tabular +data in and out of disk and networks. Most commonly used formats are +Parquet (:ref:`parquet`) and the IPC format (:ref:`ipc`). + +Creating Arrays and Tables +-------------------------- + +Arrays in Arrow are collections of data of uniform type. That allows +arrow to use the best performing implementation to store the data and +perform computation of it. So each array is meant to have data and +a type + +.. ipython:: python + + import pyarrow as pa + + days = pa.array([1, 12, 17, 23, 28], type=pa.int8()) + +multiple arrays can be combined in tables to form the columns +in tabular data according to a provided schema + +.. ipython:: python + + months = pa.array([1, 3, 5, 7, 1], type=pa.int8()) + years = pa.array([1990, 2000, 1995, 2000, 1995], type=pa.int16()) + + birthdays_table = pa.table([days, months, years], + schema=pa.schema([ + ('days', days.type), + ('months', months.type), + ('years', years.type) + ])) + + birthdays_table + +See :ref:`data` for more details. + +Saving and Loading Tables +------------------------- + +Once you have a tabular data, Arrow provides out of the box +the features to save and restore that data for common formats +like parquet + +.. ipython:: python + + import pyarrow.parquet as pq + + pq.write_table(birthdays_table, 'birthdays.parquet') + +Once you have your data on disk, loading it back is as easy, +and Arrow is heavily optimized for memory and speed so loading +data will be as quick as possible + +.. ipython:: python + + reloaded_birthdays = pq.read_table('birthdays.parquet') + + reloaded_birthdays + +Saving and loading back data in arrow is usually done through +:ref:`parquet`, :ref:`ipc`, :ref:`csv` or :ref:`json` formats. + +Performing Computations +----------------------- + +Arrow ships with a bunch of compute functions that can be applied +to its arrays, so through the compute functions it's possible to apply Review comment: :+1: added tables ########## File path: docs/source/python/getstarted.rst ########## @@ -0,0 +1,145 @@ +.. Licensed to the Apache Software Foundation (ASF) under one +.. or more contributor license agreements. See the NOTICE file +.. distributed with this work for additional information +.. regarding copyright ownership. The ASF licenses this file +.. to you under the Apache License, Version 2.0 (the +.. "License"); you may not use this file except in compliance +.. with the License. You may obtain a copy of the License at + +.. http://www.apache.org/licenses/LICENSE-2.0 + +.. Unless required by applicable law or agreed to in writing, +.. software distributed under the License is distributed on an +.. "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +.. KIND, either express or implied. See the License for the +.. specific language governing permissions and limitations +.. under the License. + +.. _getstarted: + +Getting Started +=============== + +Arrow manages data in Arrays (:class:`pyarrow.Array`), which can be Review comment: :+1: ########## File path: docs/source/python/getstarted.rst ########## @@ -0,0 +1,145 @@ +.. Licensed to the Apache Software Foundation (ASF) under one +.. or more contributor license agreements. See the NOTICE file +.. distributed with this work for additional information +.. regarding copyright ownership. The ASF licenses this file +.. to you under the Apache License, Version 2.0 (the +.. "License"); you may not use this file except in compliance +.. with the License. You may obtain a copy of the License at + +.. http://www.apache.org/licenses/LICENSE-2.0 + +.. Unless required by applicable law or agreed to in writing, +.. software distributed under the License is distributed on an +.. "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +.. KIND, either express or implied. See the License for the +.. specific language governing permissions and limitations +.. under the License. + +.. _getstarted: + +Getting Started +=============== + +Arrow manages data in Arrays (:class:`pyarrow.Array`), which can be +grouped in tables (:class:`pyarrow.Table`) to represent columns of data +in tabular data. + +Arrow also provides support for various formats to get those tabular +data in and out of disk and networks. Most commonly used formats are +Parquet (:ref:`parquet`) and the IPC format (:ref:`ipc`). + +Creating Arrays and Tables +-------------------------- + +Arrays in Arrow are collections of data of uniform type. That allows +Arrow to use the best performing implementation to store the data and +perform computations on it. So each array is meant to have data and +a type + +.. ipython:: python + + import pyarrow as pa + + days = pa.array([1, 12, 17, 23, 28], type=pa.int8()) + +Multiple arrays can be combined in tables to form the columns +in tabular data when attached to a column name + +.. ipython:: python + + months = pa.array([1, 3, 5, 7, 1], type=pa.int8()) + years = pa.array([1990, 2000, 1995, 2000, 1995], type=pa.int16()) + + birthdays_table = pa.table([days, months, years], + names=["days", "months", "years"]) + + birthdays_table + +See :ref:`data` for more details. + +Saving and Loading Tables +------------------------- + +Once you have tabular data, Arrow provides out of the box +the features to save and restore that data for common formats +like Parquet: + +.. ipython:: python + + import pyarrow.parquet as pq + + pq.write_table(birthdays_table, 'birthdays.parquet') + +Once you have your data on disk, loading it back is a single function call, +and Arrow is heavily optimized for memory and speed so loading +data will be as quick as possible + +.. ipython:: python + + reloaded_birthdays = pq.read_table('birthdays.parquet') + + reloaded_birthdays + +Saving and loading back data in arrow is usually done through +:ref:`Parquet <parquet>`, :ref:`IPC format <ipc>` (:ref:`feather`), :ref:`CSV <csv>` or +:ref:`Line-Delimited JSON <json>` formats. + +Performing Computations +----------------------- + +Arrow ships with a bunch of compute functions that can be applied +to its arrays and tables, so through the compute functions +it's possible to apply transformations to the data + +.. ipython:: python + + import pyarrow.compute as pc + + pc.value_counts(birthdays_table["years"]) + +See :ref:`compute` for a list of available compute functions and +how to use them. + +Working with large data +----------------------- + +Arrow also provides the :class:`pyarrow.dataset` api to work with Review comment: :+1: ########## File path: docs/source/python/getstarted.rst ########## @@ -0,0 +1,145 @@ +.. Licensed to the Apache Software Foundation (ASF) under one +.. or more contributor license agreements. See the NOTICE file +.. distributed with this work for additional information +.. regarding copyright ownership. The ASF licenses this file +.. to you under the Apache License, Version 2.0 (the +.. "License"); you may not use this file except in compliance +.. with the License. You may obtain a copy of the License at + +.. http://www.apache.org/licenses/LICENSE-2.0 + +.. Unless required by applicable law or agreed to in writing, +.. software distributed under the License is distributed on an +.. "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +.. KIND, either express or implied. See the License for the +.. specific language governing permissions and limitations +.. under the License. + +.. _getstarted: + +Getting Started +=============== + +Arrow manages data in Arrays (:class:`pyarrow.Array`), which can be +grouped in tables (:class:`pyarrow.Table`) to represent columns of data +in tabular data. + +Arrow also provides support for various formats to get those tabular +data in and out of disk and networks. Most commonly used formats are +Parquet (:ref:`parquet`) and the IPC format (:ref:`ipc`). Review comment: Here I mentioned the formats that are column major. As CSV and JSON are row oriented I didn't mentioned them as primary choices, but they are mentioned in the "Saving and Loading Tables" section together with the other available formats. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
