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.. currentmodule:: pyarrow.csv
.. _py-csv:

Reading and Writing CSV files
=============================

Arrow supports reading and writing columnar data from/to 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``, ``time32[s]``, ``timestamp[s]``, ``timestamp[ns]``,
  ``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``
* writing CSV files with options to configure the exact output format

Usage
-----

CSV reading and writing 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

To write CSV files, just call :func:`write_csv` with a
:class:`pyarrow.RecordBatch` or :class:`pyarrow.Table` and a path or
file-like object::

  >>> import pyarrow as pa
  >>> import pyarrow.csv as csv
  >>> csv.write_csv(table, "tips.csv")
  >>> with pa.CompressedOutputStream("tips.csv.gz", "gzip") as out:
  ...     csv.write_csv(table, out)

.. note:: The writer does not yet support all Arrow types.

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`::

    import pyarrow as pa
    import pyarrow.csv as csv

    table = csv.read_csv('tips.csv.gz', parse_options=csv.ParseOptions(
       delimiter=";",
       invalid_row_handler=skip_handler
    ))

Available parsing options are:

.. autosummary::

  ~ParseOptions.delimiter
  ~ParseOptions.quote_char
  ~ParseOptions.double_quote
  ~ParseOptions.escape_char
  ~ParseOptions.newlines_in_values
  ~ParseOptions.ignore_empty_lines
  ~ParseOptions.invalid_row_handler

.. seealso::

   For more examples see :class:`ParseOptions`.

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`::

   import pyarrow as pa
   import pyarrow.csv as csv

   table = csv.read_csv('tips.csv.gz', convert_options=csv.ConvertOptions(
       column_types={
           'total_bill': pa.decimal128(precision=10, scale=2),
           'tip': pa.decimal128(precision=10, scale=2),
       }
   ))

Available convert options are:

.. autosummary::

  ~ConvertOptions.check_utf8
  ~ConvertOptions.column_types
  ~ConvertOptions.null_values
  ~ConvertOptions.true_values
  ~ConvertOptions.false_values
  ~ConvertOptions.decimal_point
  ~ConvertOptions.timestamp_parsers
  ~ConvertOptions.strings_can_be_null
  ~ConvertOptions.quoted_strings_can_be_null
  ~ConvertOptions.auto_dict_encode
  ~ConvertOptions.auto_dict_max_cardinality
  ~ConvertOptions.include_columns
  ~ConvertOptions.include_missing_columns

.. seealso::

   For more examples see :class:`ConvertOptions`.

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

For memory-constrained environments, it is also possible to read a CSV file
one batch at a time, using :func:`open_csv`.

There are a few caveats:

1. For now, the incremental reader is always single-threaded (regardless of
   :attr:`ReadOptions.use_threads`)

2. Type inference is done on the first block and types are frozen afterwards;
   to make sure the right data types are inferred, either set
   :attr:`ReadOptions.block_size` to a large enough value, or use
   :attr:`ConvertOptions.column_types` to set the desired data types explicitly.

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::

    import pyarrow as pa
    import pyarrow.csv as csv

    table = csv.read_csv('tips.csv.gz', read_options=csv.ReadOptions(
       column_names=["animals", "n_legs", "entry"],
       skip_rows=1
    ))

Available read options are:

.. autosummary::

  ~ReadOptions.use_threads
  ~ReadOptions.block_size
  ~ReadOptions.skip_rows
  ~ReadOptions.skip_rows_after_names
  ~ReadOptions.column_names
  ~ReadOptions.autogenerate_column_names
  ~ReadOptions.encoding

.. seealso::

   For more examples see :class:`ReadOptions`.

Customized writing
------------------

To alter the default write settings in case of writing CSV files with
different conventions, you can create a :class:`WriteOptions` instance and
pass it to :func:`write_csv`::

  >>> import pyarrow as pa
  >>> import pyarrow.csv as csv
  >>> # Omit the header row (include_header=True is the default)
  >>> options = csv.WriteOptions(include_header=False)
  >>> csv.write_csv(table, "data.csv", options)

Incremental writing
-------------------

To write CSV files one batch at a time, create a :class:`CSVWriter`. This
requires the output (a path or file-like object), the schema of the data to
be written, and optionally write options as described above::

  >>> import pyarrow as pa
  >>> import pyarrow.csv as csv
  >>> with csv.CSVWriter("data.csv", table.schema) as writer:
  >>>     writer.write_table(table)

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.
