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.. 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

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.. 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

.. 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.

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:

.. 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.

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

.. 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.

Working with large data
-----------------------

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.

Continuing from here
--------------------

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/>`_