.. ipython:: python
    :suppress:

    # set custom tmp working directory for files that create data
    import os
    import tempfile

    orig_working_dir = os.getcwd()
    temp_working_dir = tempfile.mkdtemp(prefix="pyarrow-")
    os.chdir(temp_working_dir)

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

.. 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.

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 <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

.. 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.

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

.. ipython:: 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

.. ipython:: python

    birthdays_dataset = ds.dataset("savedir", format="parquet", partitioning=["years"])

    birthdays_dataset.files

and will lazily load chunks of data only when iterating over them

.. ipython:: python

    import datetime

    current_year = datetime.datetime.utcnow().year
    for table_chunk in birthdays_dataset.to_batches():
        print("AGES", pc.subtract(current_year, table_chunk["years"]))

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

.. ipython:: python
    :suppress:

    # clean-up custom working directory
    import os
    import shutil

    os.chdir(orig_working_dir)
    shutil.rmtree(temp_working_dir, ignore_errors=True)