amol- commented on a change in pull request #10999: URL: https://github.com/apache/arrow/pull/10999#discussion_r698498745
########## 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) Review comment: Fair point, I wrote that tables are arrays+schema but in reality they are arrays+names as arrays already provide type information. ########## 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) Review comment: Fair point, I wrote that tables are arrays+schema but in reality they are arrays+names as arrays already provide type information. Replaced the schema with `names=...` ########## 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. Review comment: :+1: replaced with explicit names ########## 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, Review comment: You are correct, I wanted to express that it's just 1 line of code like writing, but I couldn't come with a better phrase than "as easy". I tried rewording it now. ########## File path: docs/source/python/index.rst ########## @@ -15,12 +15,17 @@ .. specific language governing permissions and limitations .. under the License. -Python bindings -=============== +PyArrow - Apache Arrow Python bindings +====================================== This is the documentation of the Python API of Apache Arrow. For more details -on the Arrow format and other language bindings see the -:doc:`parent documentation <../index>`. +on the Arrow format and other language bindings Review comment: fixed ########## 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: I see what you mean, but I explicitly avoided using "partitioned data" because I perceive partitioning as the solution to a problem, not the problem itself. And here I was looking for a title that described what the following paragraphs are going to explain how to solve. I guess we can use "large data"? instead of "big data"? That doesn't change much from the title meaning point of view but it would avoid using a word that people explicitly map to something specific. ########## 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 Review comment: Yes, it's actually something I want to fix as we already have https://arrow.apache.org/docs/python/api/compute.html which does list compute functions in python ########## 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 Review comment: Yes, it's actually something I want to fix (the empty compute page) as we already have https://arrow.apache.org/docs/python/api/compute.html which does list compute functions in python ########## 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 -- This is an automated message from the Apache Git Service. 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