@@ -247,7 +247,11 @@ class _read_shared(TypedDict, Generic[HashableT], total=False):
247247 .. versionadded:: 1.4.0
248248
249249 The 'pyarrow' engine was added as an *experimental* engine, and some features
250- are unsupported, or may not work correctly, with this engine.
250+ are unsupported, or may not work correctly, with this engine. For example,
251+ the newlines_in_values in the ParseOptions of the pyarrow allows handling the
252+ newline characters within values when parsing csv files. However, this is not
253+ currently supported by Pandas. In this case, the 'csv' module in the pyarrow
254+ should be used instead. For more information, refer to the example.
251255converters : dict of {{Hashable : Callable}}, optional
252256 Functions for converting values in specified columns. Keys can either
253257 be column labels or column indices.
@@ -545,12 +549,25 @@ class _read_shared(TypedDict, Generic[HashableT], total=False):
545549... parse_dates=[1, 2],
546550... date_format={{'col 2': '%d/%m/%Y', 'col 3': '%a %d %b %Y'}},
547551... ) # doctest: +SKIP
548-
549552>>> df.dtypes # doctest: +SKIP
550553col 1 int64
551554col 2 datetime64[ns]
552555col 3 datetime64[ns]
553556dtype: object
557+
558+ The csv in pyarrow must be used if values have new line character.
559+
560+ >>> from pyarrow import csv
561+ >>> parse_options = csv.ParseOptions(newlines_in_values=True)
562+ >>> table = csv.read_csv("./example.csv", parse_options=parse_options)
563+ >>> df = table.to_pandas()
564+ >>> df.head()
565+ text idx
566+ 0 ab\ncd 0
567+ 1 ab\ncd 1
568+ 2 ab\ncd 2
569+ 3 ab\ncd 3
570+ 4 ab\ncd 4
554571""" # noqa: E501
555572
556573
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