docs/source/python/getstarted.rst
.. 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:
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).
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
.. code-block:: 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
.. code-block:: 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 pyarrow.Table days: int8 months: int8 years: int16
days: [[1,12,17,23,28]] months: [[1,3,5,7,1]] years: [[1990,2000,1995,2000,1995]]
See :ref:data for more details.
Once you have tabular data, Arrow provides out of the box the features to save and restore that data for common formats like Parquet:
.. code-block:: 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
.. code-block:: python
reloaded_birthdays = pq.read_table('birthdays.parquet') reloaded_birthdays pyarrow.Table days: int8 months: int8 years: int16
days: [[1,12,17,23,28]] months: [[1,3,5,7,1]] years: [[1990,2000,1995,2000,1995]]
Saving and loading back data in arrow is usually done through
:ref:Parquet <parquet>, :ref:IPC format <ipc> (:ref:feather),
:ref:CSV <py-csv> or :ref:Line-Delimited JSON <json> formats.
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
.. code-block:: python
import pyarrow.compute as pc pc.value_counts(birthdays_table["years"]) <pyarrow.lib.StructArray object at ...> -- is_valid: all not null -- child 0 type: int16 [ 1990, 2000, 1995 ] -- child 1 type: int64 [ 1, 2, 2 ]
See :ref:compute for a list of available compute functions and
how to use them.
Arrow also provides the :class:pyarrow.dataset API to work with
large data, which will handle for you partitioning of your data in
smaller chunks
.. code-block:: python
import pyarrow.dataset as ds ds.write_dataset(birthdays_table, "savedir", format="parquet", ... partitioning=ds.partitioning( ... pa.schema([birthdays_table.schema.field("years")]) ... ))
Loading back the partitioned dataset will detect the chunks
.. code-block:: python
birthdays_dataset = ds.dataset("savedir", format="parquet", partitioning=["years"]) birthdays_dataset.files ['savedir/1990/part-0.parquet', 'savedir/1995/part-0.parquet', 'savedir/2000/part-0.parquet']
and will lazily load chunks of data only when iterating over them
.. code-block:: python
current_year = 2025 for table_chunk in birthdays_dataset.to_batches(): ... print("AGES", pc.subtract(current_year, table_chunk["years"])) AGES [ 35 ] AGES [ 30, 30 ] AGES [ 25, 25 ]
For further details on how to work with big datasets, how to filter them,
how to project them, etc., refer to :ref:dataset documentation.
For digging further into Arrow, you might want to read the
:doc:PyArrow Documentation <./index> itself or the
Arrow Python Cookbook <https://arrow.apache.org/cookbook/py/>_