ENH: Add Arrow CSV Reader

asv_bench/benchmarks/io/csv.py

@@ -206,7 +206,7 @@ def time_read_csv(self, bad_date_value):
 class ReadCSVSkipRows(BaseIO):
 
     fname = "__test__.csv"
-    params = ([None, 10000], ["c", "python"])
+    params = ([None, 10000], ["c", "python", "pyarrow"])
     param_names = ["skiprows", "engine"]
 
     def setup(self, skiprows, engine):
@@ -320,7 +320,7 @@ def time_read_csv_python_engine(self, sep, decimal, float_precision):
 
 
 class ReadCSVEngine(StringIORewind):
-    params = ["c", "python"]
+    params = ["c", "python", "pyarrow"]
     param_names = ["engine"]
 
     def setup(self, engine):

doc/source/user_guide/io.rst

@@ -160,9 +160,15 @@ dtype : Type name or dict of column -> type, default ``None``
   (unsupported with ``engine='python'``). Use ``str`` or ``object`` together
   with suitable ``na_values`` settings to preserve and
   not interpret dtype.
-engine : {``'c'``, ``'python'``}
-  Parser engine to use. The C engine is faster while the Python engine is
-  currently more feature-complete.
+engine : {``'c'``, ``'python'``, ``'pyarrow'``}
+  Parser engine to use. The C and pyarrow engines are faster, while the python engine
+  is currently more feature-complete. Multithreading is currently only supported by
+  the pyarrow engine.
+
+  .. versionadded:: 1.4.0
+
+     The "pyarrow" engine was added as an *experimental* engine, and some features
+     are unsupported, or may not work correctly, with this engine.
 converters : dict, default ``None``
   Dict of functions for converting values in certain columns. Keys can either be
   integers or column labels.
@@ -1622,11 +1628,17 @@ Specifying ``iterator=True`` will also return the ``TextFileReader`` object:
 Specifying the parser engine
 ''''''''''''''''''''''''''''
 
-Under the hood pandas uses a fast and efficient parser implemented in C as well
-as a Python implementation which is currently more feature-complete. Where
-possible pandas uses the C parser (specified as ``engine='c'``), but may fall
-back to Python if C-unsupported options are specified. Currently, C-unsupported
-options include:
+Pandas currently supports three engines, the C engine, the python engine, and an experimental
+pyarrow engine (requires the ``pyarrow`` package). In general, the pyarrow engine is fastest
+on larger workloads and is equivalent in speed to the C engine on most other workloads.
+The python engine tends to be slower than the pyarrow and C engines on most workloads. However,
+the pyarrow engine is much less robust than the C engine, which lacks a few features compared to the
+Python engine.
+
+Where possible, pandas uses the C parser (specified as ``engine='c'``), but it may fall
+back to Python if C-unsupported options are specified.
+
+Currently, options unsupported by the C and pyarrow engines include:
 
 * ``sep`` other than a single character (e.g. regex separators)
 * ``skipfooter``
@@ -1635,6 +1647,32 @@ options include:
 Specifying any of the above options will produce a ``ParserWarning`` unless the
 python engine is selected explicitly using ``engine='python'``.
 
+Options that are unsupported by the pyarrow engine which are not covered by the list above include:
+
+* ``float_precision``
+* ``chunksize``
+* ``comment``
+* ``nrows``
+* ``thousands``
+* ``memory_map``
+* ``dialect``
+* ``warn_bad_lines``
+* ``error_bad_lines``
+* ``on_bad_lines``
+* ``delim_whitespace``
+* ``quoting``
+* ``lineterminator``
+* ``converters``
+* ``decimal``
+* ``iterator``
+* ``dayfirst``
+* ``infer_datetime_format``
+* ``verbose``
+* ``skipinitialspace``
+* ``low_memory``
+
+Specifying these options with ``engine='pyarrow'`` will raise a ``ValueError``.
+
 .. _io.remote:
 
 Reading/writing remote files

doc/source/whatsnew/v1.4.0.rst

@@ -78,10 +78,13 @@ Styler
 
 There are also bug fixes and deprecations listed below.
 
-.. _whatsnew_140.enhancements.enhancement2:
+.. _whatsnew_140.enhancements.pyarrow_csv_engine:
 
-enhancement2
-^^^^^^^^^^^^
+Multithreaded CSV reading with a new CSV Engine based on pyarrow
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+:func:`pandas.read_csv` now accepts ``engine="pyarrow"`` (requires at least ``pyarrow`` 0.17.0) as an argument, allowing for faster csv parsing on multicore machines
+with pyarrow installed. See the :doc:`I/O docs </user_guide/io>` for more info. (:issue:`23697`)
 
 .. _whatsnew_140.enhancements.other:
 

pandas/io/parsers/arrow_parser_wrapper.py

@@ -0,0 +1,138 @@
+from __future__ import annotations
+
+from pandas._typing import FilePathOrBuffer
+from pandas.compat._optional import import_optional_dependency
+
+from pandas.core.dtypes.inference import is_integer
+
+from pandas.core.frame import DataFrame
+
+from pandas.io.common import get_handle
+from pandas.io.parsers.base_parser import ParserBase
+
+
+class ArrowParserWrapper(ParserBase):
+    """
+    Wrapper for the pyarrow engine for read_csv()
+    """
+
+    def __init__(self, src: FilePathOrBuffer, **kwds):
+        self.kwds = kwds
+        self.src = src
+
+        ParserBase.__init__(self, kwds)
+
+        self._parse_kwds()
+
+    def _parse_kwds(self):
+        """
+        Validates keywords before passing to pyarrow.
+        """
+        encoding: str | None = self.kwds.get("encoding")
+        self.encoding = "utf-8" if encoding is None else encoding
+
+        self.usecols, self.usecols_dtype = self._validate_usecols_arg(
+            self.kwds["usecols"]
+        )
+        na_values = self.kwds["na_values"]
+        if isinstance(na_values, dict):
+            raise ValueError(
+                "The pyarrow engine doesn't support passing a dict for na_values"
+            )
+        self.na_values = list(self.kwds["na_values"])
+
+    def _get_pyarrow_options(self):
+        """
+        Rename some arguments to pass to pyarrow
+        """
+        mapping = {
+            "usecols": "include_columns",
+            "na_values": "null_values",
+            "escapechar": "escape_char",
+            "skip_blank_lines": "ignore_empty_lines",
+        }
+        for pandas_name, pyarrow_name in mapping.items():
+            if pandas_name in self.kwds and self.kwds.get(pandas_name) is not None:
+                self.kwds[pyarrow_name] = self.kwds.pop(pandas_name)
+
+        self.parse_options = {
+            option_name: option_value
+            for option_name, option_value in self.kwds.items()
+            if option_value is not None
+            and option_name
+            in ("delimiter", "quote_char", "escape_char", "ignore_empty_lines")
+        }
+        self.convert_options = {
+            option_name: option_value
+            for option_name, option_value in self.kwds.items()
+            if option_value is not None
+            and option_name
+            in ("include_columns", "null_values", "true_values", "false_values")
+        }
+        self.read_options = {
+            "autogenerate_column_names": self.header is None,
+            "skip_rows": self.header
+            if self.header is not None
+            else self.kwds["skiprows"],
+        }
+
+    def _finalize_output(self, frame: DataFrame) -> DataFrame:
+        """
+        Processes data read in based on kwargs.
+
+        Parameters
+        ----------
+        frame: DataFrame
+            The DataFrame to process.
+
+        Returns
+        -------
+        DataFrame
+            The processed DataFrame.
+        """
+        num_cols = len(frame.columns)
+        if self.header is None:
+            if self.names is None:
+                if self.prefix is not None:
+                    self.names = [f"{self.prefix}{i}" for i in range(num_cols)]
+                elif self.header is None:
+                    self.names = range(num_cols)
+            frame.columns = self.names
+        # we only need the frame not the names
+        frame.columns, frame = self._do_date_conversions(frame.columns, frame)
+        if self.index_col is not None:
+            for i, item in enumerate(self.index_col):
+                if is_integer(item):
+                    self.index_col[i] = frame.columns[item]
+            frame.set_index(self.index_col, drop=True, inplace=True)
+
+        if self.kwds.get("dtype") is not None:
+            frame = frame.astype(self.kwds.get("dtype"))
+        return frame
+
+    def read(self) -> DataFrame:
+        """
+        Reads the contents of a CSV file into a DataFrame and
+        processes it according to the kwargs passed in the
+        constructor.
+
+        Returns
+        -------
+        DataFrame
+            The DataFrame created from the CSV file.
+        """
+        pyarrow_csv = import_optional_dependency("pyarrow.csv")
+        self._get_pyarrow_options()
+
+        with get_handle(
+            self.src, "rb", encoding=self.encoding, is_text=False
+        ) as handles:
+            table = pyarrow_csv.read_csv(
+                handles.handle,
+                read_options=pyarrow_csv.ReadOptions(**self.read_options),
+                parse_options=pyarrow_csv.ParseOptions(**self.parse_options),
+                convert_options=pyarrow_csv.ConvertOptions(**self.convert_options),
+            )
+
+            frame = table.to_pandas()
+            return self._finalize_output(frame)