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pyarrow / _csv.cpython-311-aarch64-linux-musl.so
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Expected %zd from C header, got %zd from PyObject%.200s() keywords must be strings%s() got multiple values for keyword argument '%U'invalid vtable found for imported typebuiltinsboolcomplexdatetimetimetimedeltatzinfopyarrow.libIpcWriteOptionsIpcReadOptionsMessageMemoryPoolDataTypeListTypeLargeListTypeListViewTypeLargeListViewTypeMapTypeFixedSizeListTypeStructTypeDictionaryMemoDictionaryTypeTimestampTypeTime32TypeTime64TypeDurationTypeFixedSizeBinaryTypeDecimal32TypeDecimal64TypeDecimal128TypeDecimal256TypeRunEndEncodedTypeBaseExtensionTypeExtensionTypeFixedShapeTensorTypeBool8TypeOpaqueTypeUuidTypeJsonType_MetadataKeyValueMetadataFieldSchemaScalarArrayStatistics_PandasConvertibleTensorSparseCSRMatrixSparseCSCMatrixSparseCOOTensorSparseCSFTensorNullArrayBooleanArrayNumericArrayIntegerArrayFloatingPointArrayInt8ArrayUInt8ArrayInt16ArrayUInt16ArrayInt32ArrayUInt32ArrayInt64ArrayUInt64ArrayHalfFloatArrayFloatArrayDoubleArrayFixedSizeBinaryArrayDecimal32ArrayDecimal64ArrayDecimal128ArrayDecimal256ArrayStructArrayBaseListArrayListArrayLargeListArrayListViewArrayLargeListViewArrayMapArrayFixedSizeListArrayUnionArrayStringArrayBinaryArrayStringViewArrayBinaryViewArrayDictionaryArrayExtensionArrayMonthDayNanoIntervalArrayChunkedArray_TabularTableRecordBatchDeviceMemoryManagerBufferResizableBufferNativeFileBufferedInputStreamBufferedOutputStreamCompressedInputStreamCompressedOutputStreamCacheOptionsCodecStopTokenmultiple bases have vtable conflict: '%.200s' and '%.200s'%s() got an unexpected keyword argument '%U'%.200s() %stakes no keyword argumentstakes no arguments%.200s() %s (%zd given)takes exactly one argumentMissing type objectCannot convert %.200s to %.200sArgument '%.200s' has incorrect type (expected %.200s, got %.200s)%s%U__annotations__ must be set to a dict object__name__ must be set to a string object__qualname__ must be set to a string object__defaults__ must be set to a tuple objectchanges to cyfunction.__defaults__ will not currently affect the values used in function calls__kwdefaults__ must be set to a dict objectchanges to cyfunction.__kwdefaults__ will not currently affect the values used in function callsfunction's dictionary may not be deletedsetting function's dictionary to a non-dictjoin() result is too long for a Python stringcannot import name %S__int__ returned non-int (type %.200s).  The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python.__int__ returned non-int (type %.200s)keywords must be stringsunbound method %.200S() needs an argumenttoo many values to unpack (expected %zd)an integer is requiredvalue too large to convert to int32_tpyarrow/_csv.pyxpyarrow._csv.ConvertOptions.decimal_point.__get__pyarrow._csv.ConvertOptions.auto_dict_max_cardinality.__get__pyarrow._csv.ConvertOptions.auto_dict_max_cardinality.__set____del__pyarrow._csv.ParseOptions.delimiter.__get__pyarrow._csv.ParseOptions.quote_char.__get__pyarrow._csv.ParseOptions.double_quote.__set__pyarrow._csv.ParseOptions.escape_char.__get__pyarrow._csv.ParseOptions.wrappyarrow._csv.ReadOptions.block_size.__get__pyarrow._csv.ReadOptions.block_size.__set__pyarrow._csv.ReadOptions.skip_rows.__get__pyarrow._csv.ReadOptions.skip_rows.__set__pyarrow._csv.ReadOptions.skip_rows_after_names.__get__pyarrow._csv.ReadOptions.skip_rows_after_names.__set__pyarrow._csv.ReadOptions.wrappyarrow._csv.WriteOptions.batch_size.__get__pyarrow._csv.WriteOptions.delimiter.__get__<stringsource>string.from_py.__pyx_convert_string_from_py_6libcpp_6string_std__in_stringobject of type 'NoneType' has no len()hasattr(): attribute name must be stringpyarrow._csv.__pyx_unpickle__ISO8601__set_statetupleExpected %s, got %.200spyarrow._csv._ISO8601.__setstate_cython__pyarrow._csv.ConvertOptions.__init____getstate__pyarrow._csv.ReadOptions.__getstate__pyarrow._csv.ParseOptions.__getstate__pyarrow._csv.ReadOptions.use_threads.__set__pyarrow._csv.ParseOptions.ignore_empty_lines.__set__pyarrow._csv.ConvertOptions.include_missing_columns.__set__pyarrow._csv.ConvertOptions.strings_can_be_null.__set__pyarrow._csv.ConvertOptions.auto_dict_encode.__set__pyarrow._csv.ConvertOptions.quoted_strings_can_be_null.__set__pyarrow._csv.WriteOptions.include_header.__set__pyarrow._csv.ParseOptions.newlines_in_values.__set__pyarrow._csv.ReadOptions.autogenerate_column_names.__set__pyarrow._csv.ConvertOptions.check_utf8.__set__pyarrow._csv.ReadOptions.__init__pyarrow._csv.ParseOptions.__init__pyarrow._csv.ConvertOptions.__eq__pyarrow._csv.ReadOptions.__eq__pyarrow._csv.ParseOptions.__eq__pyarrow._csv.wrap_quoting_stylepyarrow._csv.WriteOptions.quoting_style.__get__instance exception may not have a separate valueraise: exception class must be a subclass of BaseExceptioncalling %R should have returned an instance of BaseException, not %Ronly single character unicode strings can be converted to Py_UCS4, got length %zdord() expected string of length 1, but %.200s foundord() expected a character, but string of length %zd foundpyarrow._csv._single_charpyarrow._csv.WriteOptions.delimiter.__set__pyarrow._csv.ParseOptions.escape_char.__set__pyarrow._csv.ParseOptions.quote_char.__set__pyarrow._csv.ParseOptions.delimiter.__set__pyarrow._csv.ConvertOptions.decimal_point.__set__pyarrow._csv.CSVStreamingReader.__init____pyx_unpickle__ISO8601pyarrow._csv.__pyx_unpickle__ISO8601pyarrow._csv.CSVWriter.__setstate_cython__pyarrow._csv.CSVWriter.__reduce_cython__pyarrow._csv.WriteOptions.__setstate_cython__pyarrow._csv.WriteOptions.__reduce_cython___raise_invalid_function_optionpyarrow._csv._raise_invalid_function_optionpyarrow._csv.CSVStreamingReader.__setstate_cython__pyarrow._csv.CSVStreamingReader.__reduce_cython__pyarrow._csv.ConvertOptions.__setstate_cython__pyarrow._csv.ConvertOptions.__reduce_cython__pyarrow._csv.ParseOptions.__setstate_cython__pyarrow._csv.ParseOptions.__reduce_cython__pyarrow._csv.ReadOptions.__setstate_cython__pyarrow._csv.ReadOptions.__reduce_cython__name '%U' is not definedpyarrow._csv._ISO8601.__reduce_cython__pyarrow._csv.unwrap_quoting_stylepyarrow._csv.WriteOptions.quoting_style.__set__basic_string: construction from null is not validstring.to_py.__pyx_convert_PyBytes_string_to_py_6libcpp_6string_std__in_stringpyarrow._csv._handle_invalid_rowequalspyarrow._csv.ConvertOptions.equalsotherpyarrow._csv.ReadOptions.equalspyarrow._csv.ParseOptions.equals__setstate__need more than %zd value%.1s to unpackpyarrow._csv.ReadOptions.__setstate__pyarrow._csv.ParseOptions.__setstate__pyarrow._csv.WriteOptions.batch_size.__set__pyarrow._csv.ConvertOptions.__getstate__pyarrow._csv.ConvertOptions.__setstate__Unable to initialize pickling for %.200sModule '_csv' has already been imported. Re-initialisation is not supported.cython_runtime__builtins__does not matchpyarrow._csvcompile time Python version %d.%d of module '%.100s' %s runtime version %d.%d_WeakrefableRecordBatchReader_CRecordBatchWriterint (arrow::Status const &)check_status arrow::MemoryPool *(struct __pyx_obj_7pyarrow_3lib_MemoryPool *)maybe_unbox_memory_poolPyObject *(PyObject *, bool, std::shared_ptr< arrow::io::InputStream>  *)get_input_streamPyObject *(PyObject *, std::shared_ptr< arrow::io::OutputStream>  *)get_writerstd::shared_ptr< arrow::io::InputStream>  (std::shared_ptr< arrow::io::InputStream> , PyObject *, PyObject *)native_transcoding_input_streamstruct __pyx_obj_7pyarrow_3lib_DataType *(PyObject *, int __pyx_skip_dispatch, struct __pyx_opt_args_7pyarrow_3lib_ensure_type *__pyx_optional_args)ensure_typePyObject *(std::shared_ptr< arrow::DataType>  const &)pyarrow_wrap_data_typePyObject *(std::shared_ptr< arrow::Schema>  const &)pyarrow_wrap_schemaPyObject *(std::shared_ptr< arrow::Table>  const &)pyarrow_wrap_tablestd::shared_ptr< arrow::DataType>  (PyObject *)pyarrow_unwrap_data_typestd::shared_ptr< arrow::Schema>  (PyObject *)pyarrow_unwrap_schemastd::shared_ptr< arrow::RecordBatch>  (PyObject *)pyarrow_unwrap_batchstd::shared_ptr< arrow::Table>  (PyObject *)pyarrow_unwrap_table__mro_entries__ must return a tuplemetaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its bases__orig_bases__init pyarrow._csvpyarrow._csv.ParseOptions.invalid_row_handler.__set__pyarrow._csv.WriteOptions.wrappyarrow._csv.WriteOptions.__init__validatepyarrow._csv.ConvertOptions.validatepyarrow._csv.ParseOptions.validatepyarrow._csv.ReadOptions.validatepyarrow._csv.WriteOptions.validatewrite_csvpyarrow._csv.write_csvmemory_pool
pyarrow._csv._get_readerpyarrow._csv.ReadOptions.column_names.__get__pyarrow._csv.ConvertOptions.include_columns.__get__pyarrow._csv.ConvertOptions.false_values.__get__pyarrow._csv.ConvertOptions.true_values.__get__pyarrow._csv.ConvertOptions.null_values.__get__pyarrow._csv.ConvertOptions.timestamp_parsers.__get__iso8601pyarrow._csv.ReadOptions.column_names.__set__pyarrow._csv.ConvertOptions.include_columns.__set__pyarrow._csv.ConvertOptions.wrappyarrow._csv.CSVStreamingReader._openpyarrow._csv.CSVWriter.__init__schemawrite_optionsvector.from_py.__pyx_convert_vector_from_py_std_3a__3a_stringvector::reservepyarrow._csv.ConvertOptions.false_values.__set__pyarrow._csv.ConvertOptions.true_values.__set__pyarrow._csv.ConvertOptions.null_values.__set__pyarrow._csv.ConvertOptions.timestamp_parsers.__set__pyarrow._csv.ConvertOptions.column_types.__get__open_csvpyarrow._csv.open_csvread_csvpyarrow._csv.read_csvpyarrow._csv.ConvertOptions.column_types.__set___cython_3_1_3.cython_function_or_method__reduce____module____dictoffset____vectorcalloffset____weaklistoffset__func_doc__doc__func_name__name____qualname__func_dict__dict__func_globals__globals__func_closure__closure__func_code__code__func_defaults__defaults____kwdefaults____annotations___is_coroutine_cython_3_1_3._common_types_metatypepyarrow._csv.CSVWriterCSVWriter(sink, Schema schema, WriteOptions write_options=None, *, MemoryPool memory_pool=None)

Writer to create a CSV file.

Parameters
----------
sink : str, path, pyarrow.OutputStream or file-like object
    The location where to write the CSV data.
schema : pyarrow.Schema
    The schema of the data to be written.
write_options : pyarrow.csv.WriteOptions
    Options to configure writing the CSV data.
memory_pool : MemoryPool, optional
    Pool for temporary allocations.pyarrow._csv.CSVStreamingReaderCSVStreamingReader()

An object that reads record batches incrementally from a CSV file.

Should not be instantiated directly by user code.pyarrow._csv._ISO8601
    A special object indicating ISO-8601 parsing.
    pyarrow._csv.WriteOptionsWriteOptions(include_header=None, *, batch_size=None, delimiter=None, quoting_style=None)

Options for writing CSV files.

Parameters
----------
include_header : bool, optional (default True)
    Whether to write an initial header line with column names
batch_size : int, optional (default 1024)
    How many rows to process together when converting and writing
    CSV data
delimiter : 1-character string, optional (default ",")
    The character delimiting individual cells in the CSV data.
quoting_style : str, optional (default "needed")
    Whether to quote values, and if so, which quoting style to use.
    The following values are accepted:

    - "needed" (default): only enclose values in quotes when needed.
    - "all_valid": enclose all valid values in quotes; nulls are not quoted.
    - "none": do not enclose any values in quotes; values containing
      special characters (such as quotes, cell delimiters or line endings)
      will raise an error.include_header
        Whether to write an initial header line with column names.
        batch_size
        How many rows to process together when converting and writing
        CSV data.
        delimiter
        The character delimiting individual cells in the CSV data.
        quoting_style
        Whether to quote values, and if so, which quoting style to use.
        The following values are accepted:

        - "needed" (default): only enclose values in quotes when needed.
        - "all_valid": enclose all valid values in quotes; nulls are not quoted.
        - "none": do not enclose any values in quotes; values containing
          special characters (such as quotes, cell delimiters or line endings)
          will raise an error.
        pyarrow._csv.ReadOptionsReadOptions(use_threads=None, *, block_size=None, skip_rows=None, skip_rows_after_names=None, column_names=None, autogenerate_column_names=None, encoding='utf8')

Options for reading CSV files.

Parameters
----------
use_threads : bool, optional (default True)
    Whether to use multiple threads to accelerate reading
block_size : int, optional
    How much bytes to process at a time from the input stream.
    This will determine multi-threading granularity as well as
    the size of individual record batches or table chunks.
    Minimum valid value for block size is 1
skip_rows : int, optional (default 0)
    The number of rows to skip before the column names (if any)
    and the CSV data.
skip_rows_after_names : int, optional (default 0)
    The number of rows to skip after the column names.
    This number can be larger than the number of rows in one
    block, and empty rows are counted.
    The order of application is as follows:
    - `skip_rows` is applied (if non-zero);
    - column names are read (unless `column_names` is set);
    - `skip_rows_after_names` is applied (if non-zero).
column_names : list, optional
    The column names of the target table.  If empty, fall back on
    `autogenerate_column_names`.
autogenerate_column_names : bool, optional (default False)
    Whether to autogenerate column names if `column_names` is empty.
    If true, column names will be of the form "f0", "f1"...
    If false, column names will be read from the first CSV row
    after `skip_rows`.
encoding : str, optional (default 'utf8')
    The character encoding of the CSV data.  Columns that cannot
    decode using this encoding can still be read as Binary.

Examples
--------

Defining an example data:

>>> import io
>>> s = "1,2,3\nFlamingo,2,2022-03-01\nHorse,4,2022-03-02\nBrittle stars,5,2022-03-03\nCentipede,100,2022-03-04"
>>> print(s)
1,2,3
Flamingo,2,2022-03-01
Horse,4,2022-03-02
Brittle stars,5,2022-03-03
Centipede,100,2022-03-04

Ignore the first numbered row and substitute it with defined
or autogenerated column names:

>>> from pyarrow import csv
>>> read_options = csv.ReadOptions(
...                column_names=["animals", "n_legs", "entry"],
...                skip_rows=1)
>>> csv.read_csv(io.BytesIO(s.encode()), read_options=read_options)
pyarrow.Table
animals: string
n_legs: int64
entry: date32[day]
----
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
n_legs: [[2,4,5,100]]
entry: [[2022-03-01,2022-03-02,2022-03-03,2022-03-04]]

>>> read_options = csv.ReadOptions(autogenerate_column_names=True,
...                                skip_rows=1)
>>> csv.read_csv(io.BytesIO(s.encode()), read_options=read_options)
pyarrow.Table
f0: string
f1: int64
f2: date32[day]
----
f0: [["Flamingo","Horse","Brittle stars","Centipede"]]
f1: [[2,4,5,100]]
f2: [[2022-03-01,2022-03-02,2022-03-03,2022-03-04]]

Remove the first 2 rows of the data:

>>> read_options = csv.ReadOptions(skip_rows_after_names=2)
>>> csv.read_csv(io.BytesIO(s.encode()), read_options=read_options)
pyarrow.Table
1: string
2: int64
3: date32[day]
----
1: [["Brittle stars","Centipede"]]
2: [[5,100]]
3: [[2022-03-03,2022-03-04]]use_threads
        Whether to use multiple threads to accelerate reading.
        block_size
        How much bytes to process at a time from the input stream.
        This will determine multi-threading granularity as well as
        the size of individual record batches or table chunks.
        skip_rows
        The number of rows to skip before the column names (if any)
        and the CSV data.
        See `skip_rows_after_names` for interaction description
        skip_rows_after_names
        The number of rows to skip after the column names.
        This number can be larger than the number of rows in one
        block, and empty rows are counted.
        The order of application is as follows:
        - `skip_rows` is applied (if non-zero);
        - column names are read (unless `column_names` is set);
        - `skip_rows_after_names` is applied (if non-zero).
        column_names
        The column names of the target table.  If empty, fall back on
        `autogenerate_column_names`.
        autogenerate_column_names
        Whether to autogenerate column names if `column_names` is empty.
        If true, column names will be of the form "f0", "f1"...
        If false, column names will be read from the first CSV row
        after `skip_rows`.
        encodingencoding: objectpyarrow._csv.ParseOptionsParseOptions(delimiter=None, *, quote_char=None, double_quote=None, escape_char=None, newlines_in_values=None, ignore_empty_lines=None, invalid_row_handler=None)

Options for parsing CSV files.

Parameters
----------
delimiter : 1-character string, optional (default ',')
    The character delimiting individual cells in the CSV data.
quote_char : 1-character string or False, optional (default '"')
    The character used optionally for quoting CSV values
    (False if quoting is not allowed).
double_quote : bool, optional (default True)
    Whether two quotes in a quoted CSV value denote a single quote
    in the data.
escape_char : 1-character string or False, optional (default False)
    The character used optionally for escaping special characters
    (False if escaping is not allowed).
newlines_in_values : bool, optional (default False)
    Whether newline characters are allowed in CSV values.
    Setting this to True reduces the performance of multi-threaded
    CSV reading.
ignore_empty_lines : bool, optional (default True)
    Whether empty lines are ignored in CSV input.
    If False, an empty line is interpreted as containing a single empty
    value (assuming a one-column CSV file).
invalid_row_handler : callable, optional (default None)
    If not None, this object is called for each CSV row that fails
    parsing (because of a mismatching number of columns).
    It should accept a single InvalidRow argument and return either
    "skip" or "error" depending on the desired outcome.

Examples
--------

Defining an example file from bytes object:

>>> import io
>>> s = (
...     "animals;n_legs;entry\n"
...     "Flamingo;2;2022-03-01\n"
...     "# Comment here:\n"
...     "Horse;4;2022-03-02\n"
...     "Brittle stars;5;2022-03-03\n"
...     "Centipede;100;2022-03-04"
... )
>>> print(s)
animals;n_legs;entry
Flamingo;2;2022-03-01
# Comment here:
Horse;4;2022-03-02
Brittle stars;5;2022-03-03
Centipede;100;2022-03-04
>>> source = io.BytesIO(s.encode())

Read the data from a file skipping rows with comments
and defining the delimiter:

>>> from pyarrow import csv
>>> def skip_comment(row):
...     if row.text.startswith("# "):
...         return 'skip'
...     else:
...         return 'error'
...
>>> parse_options = csv.ParseOptions(delimiter=";", invalid_row_handler=skip_comment)
>>> csv.read_csv(source, parse_options=parse_options)
pyarrow.Table
animals: string
n_legs: int64
entry: date32[day]
----
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
n_legs: [[2,4,5,100]]
entry: [[2022-03-01,2022-03-02,2022-03-03,2022-03-04]]quote_char
        The character used optionally for quoting CSV values
        (False if quoting is not allowed).
        double_quote
        Whether two quotes in a quoted CSV value denote a single quote
        in the data.
        escape_char
        The character used optionally for escaping special characters
        (False if escaping is not allowed).
        newlines_in_values
        Whether newline characters are allowed in CSV values.
        Setting this to True reduces the performance of multi-threaded
        CSV reading.
        ignore_empty_lines
        Whether empty lines are ignored in CSV input.
        If False, an empty line is interpreted as containing a single empty
        value (assuming a one-column CSV file).
        invalid_row_handler
        Optional handler for invalid rows.

        If not None, this object is called for each CSV row that fails
        parsing (because of a mismatching number of columns).
        It should accept a single InvalidRow argument and return either
        "skip" or "error" depending on the desired outcome.
        pyarrow._csv.ConvertOptionsConvertOptions(check_utf8=None, *, column_types=None, null_values=None, true_values=None, false_values=None, decimal_point=None, strings_can_be_null=None, quoted_strings_can_be_null=None, include_columns=None, include_missing_columns=None, auto_dict_encode=None, auto_dict_max_cardinality=None, timestamp_parsers=None)

Options for converting CSV data.

Parameters
----------
check_utf8 : bool, optional (default True)
    Whether to check UTF8 validity of string columns.
column_types : pyarrow.Schema or dict, optional
    Explicitly map column names to column types. Passing this argument
    disables type inference on the defined columns.
null_values : list, optional
    A sequence of strings that denote nulls in the data
    (defaults are appropriate in most cases). Note that by default,
    string columns are not checked for null values. To enable
    null checking for those, specify ``strings_can_be_null=True``.
true_values : list, optional
    A sequence of strings that denote true booleans in the data
    (defaults are appropriate in most cases).
false_values : list, optional
    A sequence of strings that denote false booleans in the data
    (defaults are appropriate in most cases).
decimal_point : 1-character string, optional (default '.')
    The character used as decimal point in floating-point and decimal
    data.
strings_can_be_null : bool, optional (default False)
    Whether string / binary columns can have null values.
    If true, then strings in null_values are considered null for
    string columns.
    If false, then all strings are valid string values.
quoted_strings_can_be_null : bool, optional (default True)
    Whether quoted values can be null.
    If true, then strings in "null_values" are also considered null
    when they appear quoted in the CSV file. Otherwise, quoted values
    are never considered null.
include_columns : list, optional
    The names of columns to include in the Table.
    If empty, the Table will include all columns from the CSV file.
    If not empty, only these columns will be included, in this order.
include_missing_columns : bool, optional (default False)
    If false, columns in `include_columns` but not in the CSV file will
    error out.
    If true, columns in `include_columns` but not in the CSV file will
    produce a column of nulls (whose type is selected using
    `column_types`, or null by default).
    This option is ignored if `include_columns` is empty.
auto_dict_encode : bool, optional (default False)
    Whether to try to automatically dict-encode string / binary data.
    If true, then when type inference detects a string or binary column,
    it it dict-encoded up to `auto_dict_max_cardinality` distinct values
    (per chunk), after which it switches to regular encoding.
    This setting is ignored for non-inferred columns (those in
    `column_types`).
auto_dict_max_cardinality : int, optional
    The maximum dictionary cardinality for `auto_dict_encode`.
    This value is per chunk.
timestamp_parsers : list, optional
    A sequence of strptime()-compatible format strings, tried in order
    when attempting to infer or convert timestamp values (the special
    value ISO8601() can also be given).  By default, a fast built-in
    ISO-8601 parser is used.

Examples
--------

Defining an example data:

>>> import io
>>> s = (
...     "animals,n_legs,entry,fast\n"
...     "Flamingo,2,01/03/2022,Yes\n"
...     "Horse,4,02/03/2022,Yes\n"
...     "Brittle stars,5,03/03/2022,No\n"
...     "Centipede,100,04/03/2022,No\n"
...     ",6,05/03/2022,"
... )
>>> print(s)
animals,n_legs,entry,fast
Flamingo,2,01/03/2022,Yes
Horse,4,02/03/2022,Yes
Brittle stars,5,03/03/2022,No
Centipede,100,04/03/2022,No
,6,05/03/2022,

Change the type of a column:

>>> import pyarrow as pa
>>> from pyarrow import csv
>>> convert_options = csv.ConvertOptions(column_types={"n_legs": pa.float64()})
>>> csv.read_csv(io.BytesIO(s.encode()), convert_options=convert_options)
pyarrow.Table
animals: string
n_legs: double
entry: string
fast: string
----
animals: [["Flamingo","Horse","Brittle stars","Centipede",""]]
n_legs: [[2,4,5,100,6]]
entry: [["01/03/2022","02/03/2022","03/03/2022","04/03/2022","05/03/2022"]]
fast: [["Yes","Yes","No","No",""]]

Define a date parsing format to get a timestamp type column
(in case dates are not in ISO format and not converted by default):

>>> convert_options = csv.ConvertOptions(
...                   timestamp_parsers=["%m/%d/%Y", "%m-%d-%Y"])
>>> csv.read_csv(io.BytesIO(s.encode()), convert_options=convert_options)
pyarrow.Table
animals: string
n_legs: int64
entry: timestamp[s]
fast: string
----
animals: [["Flamingo","Horse","Brittle stars","Centipede",""]]
n_legs: [[2,4,5,100,6]]
entry: [[2022-01-03 00:00:00,2022-02-03 00:00:00,2022-03-03 00:00:00,2022-04-03 00:00:00,2022-05-03 00:00:00]]
fast: [["Yes","Yes","No","No",""]]

Specify a subset of columns to be read:

>>> convert_options = csv.ConvertOptions(
...                   include_columns=["animals", "n_legs"])
>>> csv.read_csv(io.BytesIO(s.encode()), convert_options=convert_options)
pyarrow.Table
animals: string
n_legs: int64
----
animals: [["Flamingo","Horse","Brittle stars","Centipede",""]]
n_legs: [[2,4,5,100,6]]

List additional column to be included as a null typed column:

>>> convert_options = csv.ConvertOptions(
...                   include_columns=["animals", "n_legs", "location"],
...                   include_missing_columns=True)
>>> csv.read_csv(io.BytesIO(s.encode()), convert_options=convert_options)
pyarrow.Table
animals: string
n_legs: int64
location: null
----
animals: [["Flamingo","Horse","Brittle stars","Centipede",""]]
n_legs: [[2,4,5,100,6]]
location: [5 nulls]

Define columns as dictionary type (by default only the
string/binary columns are dictionary encoded):

>>> convert_options = csv.ConvertOptions(
...                   timestamp_parsers=["%m/%d/%Y", "%m-%d-%Y"],
...                   auto_dict_encode=True)
>>> csv.read_csv(io.BytesIO(s.encode()), convert_options=convert_options)
pyarrow.Table
animals: dictionary<values=string, indices=int32, ordered=0>
n_legs: int64
entry: timestamp[s]
fast: dictionary<values=string, indices=int32, ordered=0>
----
animals: [  -- dictionary:
["Flamingo","Horse","Brittle stars","Centipede",""]  -- indices:
[0,1,2,3,4]]
n_legs: [[2,4,5,100,6]]
entry: [[2022-01-03 00:00:00,2022-02-03 00:00:00,2022-03-03 00:00:00,2022-04-03 00:00:00,2022-05-03 00:00:00]]
fast: [  -- dictionary:
["Yes","No",""]  -- indices:
[0,0,1,1,2]]

Set upper limit for the number of categories. If the categories
is more than the limit, the conversion to dictionary will not
happen:

>>> convert_options = csv.ConvertOptions(
...                   include_columns=["animals"],
...                   auto_dict_encode=True,
...                   auto_dict_max_cardinality=2)
>>> csv.read_csv(io.BytesIO(s.encode()), convert_options=convert_options)
pyarrow.Table
animals: string
----
animals: [["Flamingo","Horse","Brittle stars","Centipede",""]]

Set empty strings to missing values:

>>> convert_options = csv.ConvertOptions(include_columns=["animals", "n_legs"],
...                   strings_can_be_null=True)
>>> csv.read_csv(io.BytesIO(s.encode()), convert_options=convert_options)
pyarrow.Table
animals: string
n_legs: int64
----
animals: [["Flamingo","Horse","Brittle stars","Centipede",null]]
n_legs: [[2,4,5,100,6]]

Define values to be True and False when converting a column
into a bool type:

>>> convert_options = csv.ConvertOptions(
...                   include_columns=["fast"],
...                   false_values=["No"],
...                   true_values=["Yes"])
>>> csv.read_csv(io.BytesIO(s.encode()), convert_options=convert_options)
pyarrow.Table
fast: bool
----
fast: [[true,true,false,false,null]]check_utf8
        Whether to check UTF8 validity of string columns.
        strings_can_be_null
        Whether string / binary columns can have null values.
        quoted_strings_can_be_null
        Whether quoted values can be null.
        column_types
        Explicitly map column names to column types.
        null_values
        A sequence of strings that denote nulls in the data.
        true_values
        A sequence of strings that denote true booleans in the data.
        false_values
        A sequence of strings that denote false booleans in the data.
        decimal_point
        The character used as decimal point in floating-point and decimal
        data.
        auto_dict_encode
        Whether to try to automatically dict-encode string / binary data.
        auto_dict_max_cardinality
        The maximum dictionary cardinality for `auto_dict_encode`.

        This value is per chunk.
        include_columns
        The names of columns to include in the Table.

        If empty, the Table will include all columns from the CSV file.
        If not empty, only these columns will be included, in this order.
        include_missing_columns
        If false, columns in `include_columns` but not in the CSV file will
        error out.
        If true, columns in `include_columns` but not in the CSV file will
        produce a null column (whose type is selected using `column_types`,
        or null by default).
        This option is ignored if `include_columns` is empty.
        timestamp_parsers
        A sequence of strptime()-compatible format strings, tried in order
        when attempting to infer or convert timestamp values (the special
        value ISO8601() can also be given).  By default, a fast built-in
        ISO-8601 parser is used.
        vector::_M_realloc_insert
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^1ØÐ!Ð!kÐkmÐmnØ88 1 AØ|7!Ø*¨!¨;°nÀAØ1__pyx_unpickle__ISO8601(__pyx_type, long __pyx_checksum, __pyx_state)CSVWriter.__setstate_cython__(self, __pyx_state)CSVWriter.__reduce_cython__(self)write_csv(data, output_file, write_options=None, MemoryPool memory_pool=None)

Write record batch or table to a CSV file.

Parameters
----------
data : pyarrow.RecordBatch or pyarrow.Table
    The data to write.
output_file : string, path, pyarrow.NativeFile, or file-like object
    The location where to write the CSV data.
write_options : pyarrow.csv.WriteOptions
    Options to configure writing the CSV data.
memory_pool : MemoryPool, optional
    Pool for temporary allocations.

Examples
--------

>>> import pyarrow as pa
>>> from pyarrow import csv

>>> legs = pa.array([2, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
>>> entry_date = pa.array(["01/03/2022", "02/03/2022",
...                        "03/03/2022", "04/03/2022"])
>>> table = pa.table([animals, legs, entry_date],
...                  names=["animals", "n_legs", "entry"])

>>> csv.write_csv(table, "animals.csv")

>>> write_options = csv.WriteOptions(include_header=False)
>>> csv.write_csv(table, "animals.csv", write_options=write_options)

>>> write_options = csv.WriteOptions(delimiter=";")
>>> csv.write_csv(table, "animals.csv", write_options=write_options)WriteOptions.__setstate_cython__(self, __pyx_state)WriteOptions.__reduce_cython__(self)WriteOptions.validate(self)_raise_invalid_function_option(value, description, *, exception_class=ValueError)open_csv(input_file, read_options=None, parse_options=None, convert_options=None, MemoryPool memory_pool=None)

Open a streaming reader of CSV data.

Reading using this function is always single-threaded.

Parameters
----------
input_file : string, path or file-like object
    The location of CSV data.  If a string or path, and if it ends
    with a recognized compressed file extension (e.g. ".gz" or ".bz2"),
    the data is automatically decompressed when reading.
read_options : pyarrow.csv.ReadOptions, optional
    Options for the CSV reader (see pyarrow.csv.ReadOptions constructor
    for defaults)
parse_options : pyarrow.csv.ParseOptions, optional
    Options for the CSV parser
    (see pyarrow.csv.ParseOptions constructor for defaults)
convert_options : pyarrow.csv.ConvertOptions, optional
    Options for converting CSV data
    (see pyarrow.csv.ConvertOptions constructor for defaults)
memory_pool : MemoryPool, optional
    Pool to allocate RecordBatch memory from

Returns
-------
:class:`pyarrow.csv.CSVStreamingReader`read_csv(input_file, read_options=None, parse_options=None, convert_options=None, MemoryPool memory_pool=None)

Read a Table from a stream of CSV data.

Parameters
----------
input_file : string, path or file-like object
    The location of CSV data.  If a string or path, and if it ends
    with a recognized compressed file extension (e.g. ".gz" or ".bz2"),
    the data is automatically decompressed when reading.
read_options : pyarrow.csv.ReadOptions, optional
    Options for the CSV reader (see pyarrow.csv.ReadOptions constructor
    for defaults)
parse_options : pyarrow.csv.ParseOptions, optional
    Options for the CSV parser
    (see pyarrow.csv.ParseOptions constructor for defaults)
convert_options : pyarrow.csv.ConvertOptions, optional
    Options for converting CSV data
    (see pyarrow.csv.ConvertOptions constructor for defaults)
memory_pool : MemoryPool, optional
    Pool to allocate Table memory from

Returns
-------
:class:`pyarrow.Table`
    Contents of the CSV file as a in-memory table.

Examples
--------

Defining an example file from bytes object:

>>> import io
>>> s = (
...     "animals,n_legs,entry\n"
...     "Flamingo,2,2022-03-01\n"
...     "Horse,4,2022-03-02\n"
...     "Brittle stars,5,2022-03-03\n"
...     "Centipede,100,2022-03-04"
... )
>>> print(s)
animals,n_legs,entry
Flamingo,2,2022-03-01
Horse,4,2022-03-02
Brittle stars,5,2022-03-03
Centipede,100,2022-03-04
>>> source = io.BytesIO(s.encode())

Reading from the file

>>> from pyarrow import csv
>>> csv.read_csv(source)
pyarrow.Table
animals: string
n_legs: int64
entry: date32[day]
----
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
n_legs: [[2,4,5,100]]
entry: [[2022-03-01,2022-03-02,2022-03-03,2022-03-04]]CSVStreamingReader.__setstate_cython__(self, __pyx_state)CSVStreamingReader.__reduce_cython__(self)ConvertOptions.__setstate_cython__(self, __pyx_state)ConvertOptions.__reduce_cython__(self)ConvertOptions.__setstate__(self, state)ConvertOptions.__getstate__(self)ConvertOptions.equals(self, ConvertOptions other)

Parameters
----------
other : pyarrow.csv.ConvertOptions

Returns
-------
boolConvertOptions.validate(self)_ISO8601.__setstate_cython__(self, __pyx_state)_ISO8601.__reduce_cython__(self)ParseOptions.__setstate_cython__(self, __pyx_state)ParseOptions.__reduce_cython__(self)ParseOptions.__setstate__(self, state)ParseOptions.__getstate__(self)ParseOptions.equals(self, ParseOptions other)

Parameters
----------
other : pyarrow.csv.ParseOptions

Returns
-------
boolParseOptions.validate(self)ReadOptions.__setstate_cython__(self, __pyx_state)ReadOptions.__reduce_cython__(self)ReadOptions.__setstate__(self, state)ReadOptions.__getstate__(self)ReadOptions.equals(self, ReadOptions other)

Parameters
----------
other : pyarrow.csv.ReadOptions

Returns
-------
boolReadOptions.validate(self)self.options cannot be converted to a Python object for picklingno default __reduce__ due to non-trivial __cinit__Note that Cython is deliberately stricter than PEP-484 and rejects subclasses of builtin types. If you need to pass subclasses then set the 'annotation_typing' directive to False.Invalid return value for invalid row handler: expected 'error' or 'skip', got Incompatible checksums (0x%x vs (0xe3b0c44, 0xda39a3e, 0xd41d8cd) = ())Expected callable or None, got instance of Expected Table or RecordBatch, got 'ConvertOptions.__setstate_cython__CSVStreamingReader.__setstate_cython__CSVStreamingReader.__reduce_cython__self.writer cannot be converted to a Python object for picklingself.reader cannot be converted to a Python object for pickling's constructor directly, use pyarrow.csv.open_csv() instead.
    Read a Table from a stream of CSV data.

    Parameters
    ----------
    input_file : string, path or file-like object
        The location of CSV data.  If a string or path, and if it ends
        with a recognized compressed file extension (e.g. ".gz" or ".bz2"),
        the data is automatically decompressed when reading.
    read_options : pyarrow.csv.ReadOptions, optional
        Options for the CSV reader (see pyarrow.csv.ReadOptions constructor
        for defaults)
    parse_options : pyarrow.csv.ParseOptions, optional
        Options for the CSV parser
        (see pyarrow.csv.ParseOptions constructor for defaults)
    convert_options : pyarrow.csv.ConvertOptions, optional
        Options for converting CSV data
        (see pyarrow.csv.ConvertOptions constructor for defaults)
    memory_pool : MemoryPool, optional
        Pool to allocate Table memory from

    Returns
    -------
    :class:`pyarrow.Table`
        Contents of the CSV file as a in-memory table.

    Examples
    --------

    Defining an example file from bytes object:

    >>> import io
    >>> s = (
    ...     "animals,n_legs,entry\n"
    ...     "Flamingo,2,2022-03-01\n"
    ...     "Horse,4,2022-03-02\n"
    ...     "Brittle stars,5,2022-03-03\n"
    ...     "Centipede,100,2022-03-04"
    ... )
    >>> print(s)
    animals,n_legs,entry
    Flamingo,2,2022-03-01
    Horse,4,2022-03-02
    Brittle stars,5,2022-03-03
    Centipede,100,2022-03-04
    >>> source = io.BytesIO(s.encode())

    Reading from the file

    >>> from pyarrow import csv
    >>> csv.read_csv(source)
    pyarrow.Table
    animals: string
    n_legs: int64
    entry: date32[day]
    ----
    animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
    n_legs: [[2,4,5,100]]
    entry: [[2022-03-01,2022-03-02,2022-03-03,2022-03-04]]
    Expected list of str or ISO8601 objects
    Description of an invalid row in a CSV file.

    Parameters
    ----------
    expected_columns : int
        The expected number of columns in the row.
    actual_columns : int
        The actual number of columns in the row.
    number : int or None
        The physical row number if known, otherwise None.
    text : str
        The contents of the row.
    
    Write record batch or table to a CSV file.

    Parameters
    ----------
    data : pyarrow.RecordBatch or pyarrow.Table
        The data to write.
    output_file : string, path, pyarrow.NativeFile, or file-like object
        The location where to write the CSV data.
    write_options : pyarrow.csv.WriteOptions
        Options to configure writing the CSV data.
    memory_pool : MemoryPool, optional
        Pool for temporary allocations.

    Examples
    --------

    >>> import pyarrow as pa
    >>> from pyarrow import csv

    >>> legs = pa.array([2, 4, 5, 100])
    >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
    >>> entry_date = pa.array(["01/03/2022", "02/03/2022",
    ...                        "03/03/2022", "04/03/2022"])
    >>> table = pa.table([animals, legs, entry_date],
    ...                  names=["animals", "n_legs", "entry"])

    >>> csv.write_csv(table, "animals.csv")

    >>> write_options = csv.WriteOptions(include_header=False)
    >>> csv.write_csv(table, "animals.csv", write_options=write_options)

    >>> write_options = csv.WriteOptions(delimiter=";")
    >>> csv.write_csv(table, "animals.csv", write_options=write_options)
    WriteOptions.__setstate_cython__ParseOptions.__setstate_cython__ConvertOptions.__reduce_cython___raise_invalid_function_optionReadOptions.__setstate_cython__WriteOptions.__reduce_cython__ParseOptions.__reduce_cython__Expecting an ASCII characterReadOptions.__reduce_cython__CSVWriter.__setstate_cython__quoted_strings_can_be_nullautogenerate_column_namesauto_dict_max_cardinality_ISO8601.__setstate_cython__ConvertOptions.__setstate__ConvertOptions.__getstate__CSVWriter.__reduce_cython__include_missing_columnsParseOptions.__setstate__ParseOptions.__getstate___ISO8601.__reduce_cython__ConvertOptions.validateReadOptions.__setstate__ReadOptions.__getstate__skip_rows_after_names__pyx_unpickle__ISO8601WriteOptions.validateParseOptions.validateConvertOptions.equalsReadOptions.validatewrite_csv (line 1453)strings_can_be_nullinvalid_row_handlerParseOptions.equalsread_csv (line 1184)newlines_in_valuesignore_empty_linescline_in_tracebackasyncio.coroutinesReadOptions.equalsCSVStreamingReadertimestamp_parsersc_convert_optionsSignalStopHandlerpyarrow/_csv.pyxexpected_columnsauto_dict_encode__setstate_cython__quoting_style__pyx_PickleErrorinclude_columnsexception_classconvert_optionscollections.abcc_write_optionsc_parse_options" is not a valid include_headerc_read_optionsactual_columnsConvertOptionsAssertionErrorwrite_options__reduce_cython__quoting styleparse_optionsdecimal_pointc_memory_pooluse_setstate<stringsource>stop_handlerread_options__pyx_checksumpyarrow._csv_is_coroutinefalse_valuesdouble_quotecolumn_typescolumn_namesWriteOptionsParseOptions_InvalidRowuse_threadstrue_valuespyarrow.liboutput_filenull_values__mro_entries__memory_poolescape_chardescriptioncollectionsReadOptionsPickleErrorDo not call stop_tokenquote_char__pyx_vtable____pyx_resultnamedtupleio_contextinput_filecheck_utf8block_sizebatch_sizeValueErrorInvalidRowwrite_csvskip_rows__reduce_ex____pyx_state__metaclass__isenabledfrombytesdelimiterall_validTypeError_ISO8601CSVWritervalidate__setstate____set_name__read_csv__qualname____pyx_typeopen_csv__module____getstate__encodingadd_notetobytes__prepare__disableMappingISO8601updatestreamschema__reduce__readerpicklenumberneeded__name__moduleequalsenable_dictvaluetablestate__slots__otheritemserror__enter____class__batchutf8typetext__test__skipsinkselfnonename__main____func____exit____dict__datapop__new____doc__gc?.'"FN5arrow3csv16InvalidRowResultEP7_objectRKNS0_10InvalidRowEEPFN5arrow3csv16InvalidRowResultEP7_objectRKNS0_10InvalidRowEE@;üøýÿtýÿ|ýÿÀ<ýÿd
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