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arrow-nightlies / pyarrow python
Repository URL to install this package:
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Version:
22.0.0.dev96 ▾
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pyarrow
/
_csv.cpython-39-darwin.so
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ºþ Âw _ name pyarrow._csv pyarrow/_csv.pyx <stringsource> " AssertionError CSVStreamingReader CSVStreamingReader.__reduce_cython__ CSVStreamingReader.__setstate_cython__ CSVWriter CSVWriter.__reduce_cython__ CSVWriter.__setstate_cython__ ConvertOptions ConvertOptions.__getstate__ ConvertOptions.__reduce_cython__ ConvertOptions.__setstate__ ConvertOptions.__setstate_cython__ ConvertOptions.equals ConvertOptions.validate
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.
Do not call Expected Table or RecordBatch, got ' Expected callable or None, got instance of Expected list of str or ISO8601 objects Expecting an ASCII character ISO8601 _ISO8601 _ISO8601.__reduce_cython__ _ISO8601.__setstate_cython__ Incompatible checksums (0x%x vs (0xe3b0c44, 0xda39a3e, 0xd41d8cd) = ()) InvalidRow _InvalidRow Invalid return value for invalid row handler: expected 'error' or 'skip', got Mapping 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. ParseOptions ParseOptions.__getstate__ ParseOptions.__reduce_cython__ ParseOptions.__setstate__ ParseOptions.__setstate_cython__ ParseOptions.equals ParseOptions.validate PickleError ReadOptions ReadOptions.__getstate__ ReadOptions.__reduce_cython__ ReadOptions.__setstate__ ReadOptions.__setstate_cython__ ReadOptions.equals ReadOptions.validate
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]]
SignalStopHandler TypeError ValueError WriteOptions WriteOptions.__reduce_cython__ WriteOptions.__setstate_cython__ WriteOptions.validate
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)
' . ? actual_columns add_note all_valid asyncio.coroutines batch c_convert_options c_memory_pool c_parse_options c_read_options c_write_options __class__ cline_in_traceback collections collections.abc convert_options data description _dict disable enable __enter__ error exception_class __exit__ expected_columns frombytes __func__ gc input_file io_context " is not a valid isenabled items __main__ __metaclass__ module __mro_entries__ namedtuple needed __new__ no default __reduce__ due to non-trivial __cinit__ none number output_file parse_options pickle pop __prepare__ __pyx_PickleError __pyx_checksum __pyx_result __pyx_state __pyx_type __pyx_vtable__ quoting style read_csv (line 1184) read_options reader __reduce_ex__ 's constructor directly, use pyarrow.csv.open_csv() instead. self self.options cannot be converted to a Python object for pickling self.reader cannot be converted to a Python object for pickling self.writer cannot be converted to a Python object for pickling __set_name__ sink skip __slots__ state stop_handler stop_token stream table __test__ text tobytes update use_setstate utf8 value write_csv (line 1453) __module__ __reduce__ __doc__ __name__ __qualname__ __dict__ _is_coroutine pyarrow.lib validate ConvertOptions.validate(self) equals ConvertOptions.equals(self, ConvertOptions other)
Parameters
----------
other : pyarrow.csv.ConvertOptions
Returns
-------
bool __getstate__ ConvertOptions.__getstate__(self) __setstate__ ConvertOptions.__setstate__(self, state) __reduce_cython__ ConvertOptions.__reduce_cython__(self) __setstate_cython__ ConvertOptions.__setstate_cython__(self, __pyx_state) other check_utf8 strings_can_be_null quoted_strings_can_be_null column_types null_values true_values false_values decimal_point auto_dict_encode auto_dict_max_cardinality include_columns include_missing_columns timestamp_parsers ParseOptions.validate(self) ParseOptions.equals(self, ParseOptions other)
Parameters
----------
other : pyarrow.csv.ParseOptions
Returns
-------
bool ParseOptions.__getstate__(self) ParseOptions.__setstate__(self, state) ParseOptions.__reduce_cython__(self) ParseOptions.__setstate_cython__(self, __pyx_state) delimiter quote_char double_quote escape_char newlines_in_values ignore_empty_lines invalid_row_handler NSt3__110__function6__funcIPFN5arrow3csv16InvalidRowResultEP7_objectRKNS3_10InvalidRowEENS_9allocatorISB_EESA_EE NSt3__110__function6__baseIFN5arrow3csv16InvalidRowResultEP7_objectRKNS3_10InvalidRowEEEE PFN5arrow3csv16InvalidRowResultEP7_objectRKNS0_10InvalidRowEE FN5arrow3csv16InvalidRowResultEP7_objectRKNS0_10InvalidRowEE ReadOptions.validate(self) ReadOptions.equals(self, ReadOptions other)
Parameters
----------
other : pyarrow.csv.ReadOptions
Returns
-------
bool ReadOptions.__getstate__(self) ReadOptions.__setstate__(self, state) ReadOptions.__reduce_cython__(self) ReadOptions.__setstate_cython__(self, __pyx_state) use_threads block_size skip_rows skip_rows_after_names column_names autogenerate_column_names encoding WriteOptions.validate(self) WriteOptions.__reduce_cython__(self) WriteOptions.__setstate_cython__(self, __pyx_state) include_header batch_size quoting_style _ISO8601.__reduce_cython__(self) _ISO8601.__setstate_cython__(self, __pyx_state) CSVStreamingReader.__reduce_cython__(self) CSVStreamingReader.__setstate_cython__(self, __pyx_state) schema CSVWriter.__reduce_cython__(self) CSVWriter.__setstate_cython__(self, __pyx_state) write_options memory_pool type read_csv 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]] open_csv 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` _raise_invalid_function_option _raise_invalid_function_option(value, description, *, exception_class=ValueError) write_csv 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) __pyx_unpickle__ISO8601 __pyx_unpickle__ISO8601(__pyx_type, long __pyx_checksum, __pyx_state) _csv loader __loader__ origin __file__ parent __package__ submodule_search_locations __path__ Interpreter change detected - this module can only be loaded into one interpreter per process. Module '_csv' has already been imported. Re-initialisation is not supported. builtins cython_runtime __builtins__ __orig_bases__ init pyarrow._csv compile time Python version %d.%d of module '%.100s' %s runtime version %d.%d does not match _cython_3_1_3amsendbackport Shared Cython type %.200s is not a type object Shared Cython type %.200s has the wrong size, try recompiling _cython_3_1_3amsendbackport._common_types_metatype _cython_3_1_3amsendbackport.cython_function_or_method <cyfunction %U at %p> unbound method %.200S() needs an argument keywords must be strings takes no arguments takes exactly one argument Bad call flags for CyFunction takes no keyword arguments %.200s() %s (%zd given) %.200s() %s __dictoffset__ __vectorcalloffset__ __weaklistoffset__ func_doc func_name func_dict func_globals __globals__ func_closure __closure__ func_code __code__ func_defaults __defaults__ __kwdefaults__ __annotations__ __name__ must be set to a string object __qualname__ must be set to a string object function's dictionary may not be deleted setting function's dictionary to a non-dict __defaults__ must be set to a tuple object changes to cyfunction.__defaults__ will not currently affect the values used in function calls __kwdefaults__ must be set to a dict object changes to cyfunction.__kwdefaults__ will not currently affect the values used in function calls __annotations__ must be set to a dict object _Weakrefable RecordBatchReader _CRecordBatchWriter %.200s.%.200s is not a type object %.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject pyarrow._csv.ConvertOptions.wrap pyarrow._csv.ConvertOptions ConvertOptions(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]] pyarrow._csv.ConvertOptions.__eq__ at least at most exactly %.200s() takes %.8s %zd positional argument%.1s (%zd given) s %s() got an unexpected keyword argument '%U' pyarrow._csv.ConvertOptions.validate pyarrow._csv.ConvertOptions.equals %s() got multiple values for keyword argument '%U' %.200s() keywords must be strings Missing type object Argument '%.200s' has incorrect type (expected %.200s, got %.200s)%s%U pyarrow._csv.ConvertOptions.__getstate__ pyarrow._csv.ConvertOptions.__setstate__ too many values to unpack (expected %zd) need more than %zd value%.1s to unpack pyarrow._csv.ConvertOptions.__reduce_cython__ instance exception may not have a separate value calling %R should have returned an instance of BaseException, not %R raise: exception class must be a subclass of BaseException pyarrow._csv.ConvertOptions.__setstate_cython__
Whether to check UTF8 validity of string columns.
Whether string / binary columns can have null values.
Whether quoted values can be null.
Explicitly map column names to column types.
A sequence of strings that denote nulls in the data.
A sequence of strings that denote true booleans in the data.
A sequence of strings that denote false booleans in the data.
The character used as decimal point in floating-point and decimal
data.
Whether to try to automatically dict-encode string / binary data.
The maximum dictionary cardinality for `auto_dict_encode`.
This value is per chunk.
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.
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.
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.
__del__ pyarrow._csv.ConvertOptions.check_utf8.__set__ pyarrow._csv.ConvertOptions.strings_can_be_null.__set__ pyarrow._csv.ConvertOptions.quoted_strings_can_be_null.__set__ pyarrow._csv.ConvertOptions.column_types.__get__ string.to_py.__pyx_convert_PyBytes_string_to_py_6libcpp_6string_std__in_string pyarrow._csv.ConvertOptions.column_types.__set__ string.from_py.__pyx_convert_string_from_py_6libcpp_6string_std__in_string basic_string Unknown exception pyarrow._csv.ConvertOptions.null_values.__get__ vector pyarrow._csv.ConvertOptions.null_values.__set__ vector.from_py.__pyx_convert_vector_from_py_std_3a__3a_string pyarrow._csv.ConvertOptions.true_values.__get__ pyarrow._csv.ConvertOptions.true_values.__set__ pyarrow._csv.ConvertOptions.false_values.__get__ pyarrow._csv.ConvertOptions.false_values.__set__ pyarrow._csv.ConvertOptions.decimal_point.__get__ pyarrow._csv.ConvertOptions.decimal_point.__set__ pyarrow._csv._single_char only single character unicode strings can be converted to Py_UCS4, got length %zd ord() expected string of length 1, but %.200s found ord() expected a character, but string of length %zd found pyarrow._csv.ConvertOptions.auto_dict_encode.__set__ pyarrow._csv.ConvertOptions.auto_dict_max_cardinality.__get__ pyarrow._csv.ConvertOptions.auto_dict_max_cardinality.__set__ value too large to convert to int32_t an integer is required __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) pyarrow._csv.ConvertOptions.include_columns.__get__ pyarrow._csv.ConvertOptions.include_columns.__set__ pyarrow._csv.ConvertOptions.include_missing_columns.__set__ strptime iso8601 pyarrow._csv.ConvertOptions.timestamp_parsers.__get__ pyarrow._csv.ConvertOptions.timestamp_parsers.__set__ __init__ pyarrow._csv.ConvertOptions.__init__ base class '%.200s' is not a heap type extension type '%.200s' has no __dict__ slot, but base type '%.200s' has: either add 'cdef dict __dict__' to the extension type or add '__slots__ = [...]' to the base type multiple bases have vtable conflict: '%.200s' and '%.200s' invalid vtable found for imported type Unable to initialize pickling for %.200s pyarrow._csv.ParseOptions.wrap pyarrow._csv.ParseOptions ParseOptions(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]] pyarrow._csv.ParseOptions.__eq__ pyarrow._csv.ParseOptions.validate pyarrow._csv.ParseOptions.equals pyarrow._csv.ParseOptions.__getstate__ pyarrow._csv.ParseOptions.__setstate__ pyarrow._csv.ParseOptions.__reduce_cython__ pyarrow._csv.ParseOptions.__setstate_cython__
The character delimiting individual cells in the CSV data.
The character used optionally for quoting CSV values
(False if quoting is not allowed).
Whether two quotes in a quoted CSV value denote a single quote
in the data.
The character used optionally for escaping special characters
(False if escaping is not allowed).
Whether newline characters are allowed in CSV values.
Setting this to True reduces the performance of multi-threaded
CSV reading.
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).
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.ParseOptions.delimiter.__get__ pyarrow._csv.ParseOptions.delimiter.__set__ pyarrow._csv.ParseOptions.quote_char.__get__ pyarrow._csv.ParseOptions.quote_char.__set__ pyarrow._csv.ParseOptions.double_quote.__set__ pyarrow._csv.ParseOptions.escape_char.__get__ pyarrow._csv.ParseOptions.escape_char.__set__ pyarrow._csv.ParseOptions.newlines_in_values.__set__ pyarrow._csv.ParseOptions.ignore_empty_lines.__set__ pyarrow._csv.ParseOptions.invalid_row_handler.__set__ pyarrow._csv._handle_invalid_row pyarrow._csv.ParseOptions.__init__ pyarrow._csv.ReadOptions.wrap pyarrow._csv.ReadOptions ReadOptions(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]] pyarrow._csv.ReadOptions.__eq__ pyarrow._csv.ReadOptions.validate pyarrow._csv.ReadOptions.equals pyarrow._csv.ReadOptions.__getstate__ pyarrow._csv.ReadOptions.__setstate__ pyarrow._csv.ReadOptions.__reduce_cython__ pyarrow._csv.ReadOptions.__setstate_cython__
Whether to use multiple threads to accelerate reading.
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.
The number of rows to skip before the column names (if any)
and the CSV data.
See `skip_rows_after_names` for interaction description
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).
The column names of the target table. If empty, fall back on
`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`.
encoding: object pyarrow._csv.ReadOptions.use_threads.__set__ pyarrow._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.column_names.__get__ pyarrow._csv.ReadOptions.column_names.__set__ pyarrow._csv.ReadOptions.autogenerate_column_names.__set__ pyarrow._csv.ReadOptions.__init__ pyarrow._csv.WriteOptions.wrap pyarrow._csv.WriteOptions WriteOptions(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. pyarrow._csv.WriteOptions.validate pyarrow._csv.WriteOptions.__reduce_cython__ pyarrow._csv.WriteOptions.__setstate_cython__
Whether to write an initial header line with column names.
How many rows to process together when converting and writing
CSV data.
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.WriteOptions.include_header.__set__ pyarrow._csv.WriteOptions.batch_size.__get__ pyarrow._csv.WriteOptions.batch_size.__set__ pyarrow._csv.WriteOptions.delimiter.__get__ pyarrow._csv.WriteOptions.delimiter.__set__ pyarrow._csv.WriteOptions.quoting_style.__get__ pyarrow._csv.wrap_quoting_style pyarrow._csv.WriteOptions.quoting_style.__set__ pyarrow._csv.unwrap_quoting_style pyarrow._csv.WriteOptions.__init__ pyarrow._csv._ISO8601
A special object indicating ISO-8601 parsing.
pyarrow._csv._ISO8601.__reduce_cython__ pyarrow._csv._ISO8601.__setstate_cython__ tuple Expected %s, got %.200s object of type 'NoneType' has no len() pyarrow._csv.__pyx_unpickle__ISO8601__set_state hasattr(): attribute name must be string pyarrow._csv.CSVStreamingReader._open Cannot convert %.200s to %.200s pyarrow._csv.CSVStreamingReader CSVStreamingReader()
An object that reads record batches incrementally from a CSV file.
Should not be instantiated directly by user code. pyarrow._csv.CSVStreamingReader.__reduce_cython__ pyarrow._csv.CSVStreamingReader.__setstate_cython__ pyarrow._csv.CSVStreamingReader.__init__ join() result is too long for a Python string pyarrow._csv.CSVWriter CSVWriter(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.CSVWriter.__reduce_cython__ pyarrow._csv.CSVWriter.__setstate_cython__ pyarrow._csv.CSVWriter.__init__
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