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.. with the License.  You may obtain a copy of the License at

..   http://www.apache.org/licenses/LICENSE-2.0

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.. currentmodule:: pyarrow.csv
.. _csv:

Reading CSV files
=================

Arrow supports reading columnar data from CSV files.
The features currently offered are the following:

* multi-threaded or single-threaded reading
* automatic decompression of input files (based on the filename extension,
  such as ``my_data.csv.gz``)
* fetching column names from the first row in the CSV file
* column-wise type inference and conversion to one of ``null``, ``int64``,
  ``float64``, ``date32``, ``timestamp[s]``, ``string`` or ``binary`` data
* opportunistic dictionary encoding of ``string`` and ``binary`` columns
  (disabled by default)
* detecting various spellings of null values such as ``NaN`` or ``#N/A``

Usage
-----

CSV reading functionality is available through the :mod:`pyarrow.csv` module.
In many cases, you will simply call the :func:`read_csv` function
with the file path you want to read from::

   >>> from pyarrow import csv
   >>> fn = 'tips.csv.gz'
   >>> table = csv.read_csv(fn)
   >>> table
   pyarrow.Table
   total_bill: double
   tip: double
   sex: string
   smoker: string
   day: string
   time: string
   size: int64
   >>> len(table)
   244
   >>> df = table.to_pandas()
   >>> df.head()
      total_bill   tip     sex smoker  day    time  size
   0       16.99  1.01  Female     No  Sun  Dinner     2
   1       10.34  1.66    Male     No  Sun  Dinner     3
   2       21.01  3.50    Male     No  Sun  Dinner     3
   3       23.68  3.31    Male     No  Sun  Dinner     2
   4       24.59  3.61  Female     No  Sun  Dinner     4

Customized parsing
------------------

To alter the default parsing settings in case of reading CSV files with an
unusual structure, you should create a :class:`ParseOptions` instance
and pass it to :func:`read_csv`.

Customized conversion
---------------------

To alter how CSV data is converted to Arrow types and data, you should create
a :class:`ConvertOptions` instance and pass it to :func:`read_csv`.

Incremental reading
-------------------

For memory-constrained environments, it is also possible to read a CSV file
one batch at a time, using :func:`open_csv`.  It currently doesn't support
parallel reading.

Character encoding
------------------

By default, CSV files are expected to be encoded in UTF8.  Non-UTF8 data
is accepted for ``binary`` columns.  The encoding can be changed using
the :class:`ReadOptions` class.

Performance
-----------

Due to the structure of CSV files, one cannot expect the same levels of
performance as when reading dedicated binary formats like
:ref:`Parquet <Parquet>`.  Nevertheless, Arrow strives to reduce the
overhead of reading CSV files.  A reasonable expectation is at least
100 MB/s per core on a performant desktop or laptop computer (measured
in source CSV bytes, not target Arrow data bytes).

Performance options can be controlled through the :class:`ReadOptions` class.
Multi-threaded reading is the default for highest performance, distributing
the workload efficiently over all available cores.

.. note::
   The number of concurrent threads is automatically inferred by Arrow.
   You can inspect and change it using the :func:`~pyarrow.cpu_count()`
   and :func:`~pyarrow.set_cpu_count()` functions, respectively.