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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-310-aarch64-linux-gnu.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 type builtins bool complex datetime time timedelta tzinfo pyarrow.lib IpcWriteOptions IpcReadOptions Message MemoryPool DataType ListType LargeListType ListViewType LargeListViewType MapType FixedSizeListType StructType DictionaryMemo DictionaryType TimestampType Time32Type Time64Type DurationType FixedSizeBinaryType Decimal32Type Decimal64Type Decimal128Type Decimal256Type RunEndEncodedType BaseExtensionType ExtensionType FixedShapeTensorType Bool8Type OpaqueType UuidType JsonType _Metadata KeyValueMetadata Field Schema Scalar ArrayStatistics _PandasConvertible Tensor SparseCSRMatrix SparseCSCMatrix SparseCOOTensor SparseCSFTensor NullArray BooleanArray NumericArray IntegerArray FloatingPointArray Int8Array UInt8Array Int16Array UInt16Array Int32Array UInt32Array Int64Array UInt64Array HalfFloatArray FloatArray DoubleArray FixedSizeBinaryArray Decimal32Array Decimal64Array Decimal128Array Decimal256Array StructArray BaseListArray ListArray LargeListArray ListViewArray LargeListViewArray MapArray FixedSizeListArray UnionArray StringArray BinaryArray StringViewArray BinaryViewArray DictionaryArray ExtensionArray MonthDayNanoIntervalArray ChunkedArray _Tabular Table RecordBatch Device MemoryManager Buffer ResizableBuffer NativeFile BufferedInputStream BufferedOutputStream CompressedInputStream CompressedOutputStream CacheOptions Codec StopToken multiple bases have vtable conflict: '%.200s' and '%.200s' %s() got an unexpected keyword argument '%U' %.200s() %s takes no keyword arguments takes no arguments %.200s() %s (%zd given) takes exactly one argument Missing type object Cannot convert %.200s to %.200s Argument '%.200s' has incorrect type (expected %.200s, got %.200s)%s%U __annotations__ must be set to a dict object __qualname__ must be set to a string object __name__ must be set to a string object __kwdefaults__ must be set to a dict object changes to cyfunction.__kwdefaults__ will not currently affect the values used in function calls __defaults__ must be set to a tuple object changes to cyfunction.__defaults__ will not currently affect the values used in function calls function's dictionary may not be deleted setting function's dictionary to a non-dict join() result is too long for a Python string cannot 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 strings unbound method %.200S() needs an argument too many values to unpack (expected %zd) an integer is required value too large to convert to int32_t pyarrow/_csv.pyx pyarrow._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.escape_char.__get__ pyarrow._csv.ParseOptions.wrap pyarrow._csv.ReadOptions.block_size.__get__ pyarrow._csv.ReadOptions.block_size.__set__ pyarrow._csv.ReadOptions.skip_rows.__get__ pyarrow._csv.ReadOptions.skip_rows_after_names.__get__ pyarrow._csv.ReadOptions.skip_rows_after_names.__set__ pyarrow._csv.ReadOptions.autogenerate_column_names.__set__ pyarrow._csv.ReadOptions.wrap pyarrow._csv.WriteOptions.batch_size.__get__ pyarrow._csv.WriteOptions.batch_size.__set__ pyarrow._csv.WriteOptions.delimiter.__get__ <stringsource> string.from_py.__pyx_convert_string_from_py_6libcpp_6string_std__in_string object of type 'NoneType' has no len() hasattr(): attribute name must be string pyarrow._csv.__pyx_unpickle__ISO8601__set_state tuple Expected %s, got %.200s pyarrow._csv._ISO8601.__setstate_cython__ pyarrow._csv.ConvertOptions.__eq__ pyarrow._csv.ConvertOptions.__init__ pyarrow._csv.ParseOptions.__eq__ pyarrow._csv.ReadOptions.__eq__ __getstate__ pyarrow._csv.ReadOptions.__getstate__ pyarrow._csv.ParseOptions.__getstate__ pyarrow._csv.ReadOptions.use_threads.__set__ pyarrow._csv.ConvertOptions.include_missing_columns.__set__ pyarrow._csv.ParseOptions.double_quote.__set__ pyarrow._csv.WriteOptions.include_header.__set__ pyarrow._csv.ParseOptions.ignore_empty_lines.__set__ pyarrow._csv.ConvertOptions.strings_can_be_null.__set__ pyarrow._csv.ConvertOptions.check_utf8.__set__ pyarrow._csv.ConvertOptions.quoted_strings_can_be_null.__set__ pyarrow._csv.ConvertOptions.auto_dict_encode.__set__ pyarrow._csv.ParseOptions.newlines_in_values.__set__ pyarrow._csv.ReadOptions.__init__ pyarrow._csv.ParseOptions.__init__ pyarrow._csv.wrap_quoting_style pyarrow._csv.WriteOptions.quoting_style.__get__ instance exception may not have a separate value raise: exception class must be a subclass of BaseException calling %R should have returned an instance of BaseException, not %R 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._single_char pyarrow._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__ISO8601 pyarrow._csv.__pyx_unpickle__ISO8601 pyarrow._csv.CSVWriter.__setstate_cython__ pyarrow._csv.CSVWriter.__reduce_cython__ pyarrow._csv.WriteOptions.__setstate_cython__ pyarrow._csv.WriteOptions.__reduce_cython__ _raise_invalid_function_option pyarrow._csv._raise_invalid_function_option pyarrow._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 defined pyarrow._csv._ISO8601.__reduce_cython__ pyarrow._csv.unwrap_quoting_style pyarrow._csv.WriteOptions.quoting_style.__set__ basic_string: construction from null is not valid string.to_py.__pyx_convert_PyBytes_string_to_py_6libcpp_6string_std__in_string pyarrow._csv._handle_invalid_row equals pyarrow._csv.ConvertOptions.equals other __setstate__ need more than %zd value%.1s to unpack pyarrow._csv.ConvertOptions.__setstate__ pyarrow._csv.ReadOptions.equals pyarrow._csv.ParseOptions.equals pyarrow._csv.ConvertOptions.__getstate__ pyarrow._csv.ReadOptions.skip_rows.__set__ pyarrow._csv.ReadOptions.__setstate__ pyarrow._csv.ParseOptions.__setstate__ Unable to initialize pickling for %.200s Module '_csv' has already been imported. Re-initialisation is not supported. cython_runtime __builtins__ does not match pyarrow._csv compile time Python version %d.%d of module '%.100s' %s runtime version %d.%d _Weakrefable RecordBatchReader _CRecordBatchWriter int (arrow::Status const &) check_status arrow::MemoryPool *(struct __pyx_obj_7pyarrow_3lib_MemoryPool *) maybe_unbox_memory_pool PyObject *(PyObject *, bool, std::shared_ptr< arrow::io::InputStream> *) get_input_stream PyObject *(PyObject *, std::shared_ptr< arrow::io::OutputStream> *) get_writer std::shared_ptr< arrow::io::InputStream> (std::shared_ptr< arrow::io::InputStream> , PyObject *, PyObject *) native_transcoding_input_stream struct __pyx_obj_7pyarrow_3lib_DataType *(PyObject *, int __pyx_skip_dispatch, struct __pyx_opt_args_7pyarrow_3lib_ensure_type *__pyx_optional_args) ensure_type PyObject *(std::shared_ptr< arrow::DataType> const &) pyarrow_wrap_data_type PyObject *(std::shared_ptr< arrow::Schema> const &) pyarrow_wrap_schema PyObject *(std::shared_ptr< arrow::Table> const &) pyarrow_wrap_table std::shared_ptr< arrow::DataType> (PyObject *) pyarrow_unwrap_data_type std::shared_ptr< arrow::Schema> (PyObject *) pyarrow_unwrap_schema std::shared_ptr< arrow::RecordBatch> (PyObject *) pyarrow_unwrap_batch std::shared_ptr< arrow::Table> (PyObject *) pyarrow_unwrap_table __mro_entries__ must return a tuple metaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its bases __orig_bases__ init pyarrow._csv pyarrow._csv.ParseOptions.invalid_row_handler.__set__ pyarrow._csv.WriteOptions.wrap pyarrow._csv.WriteOptions.__init__ validate pyarrow._csv.ConvertOptions.validate pyarrow._csv.ReadOptions.validate pyarrow._csv.ParseOptions.validate pyarrow._csv.WriteOptions.validate write_csv pyarrow._csv.write_csv memory_pool
pyarrow._csv._get_reader pyarrow._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__ iso8601 pyarrow._csv.ReadOptions.column_names.__set__ pyarrow._csv.ConvertOptions.include_columns.__set__ pyarrow._csv.ConvertOptions.wrap pyarrow._csv.CSVStreamingReader._open pyarrow._csv.CSVWriter.__init__ schema write_options vector.from_py.__pyx_convert_vector_from_py_std_3a__3a_string vector::reserve pyarrow._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_csv pyarrow._csv.open_csv read_csv pyarrow._csv.read_csv pyarrow._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_metatype 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.CSVStreamingReader CSVStreamingReader()
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.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. 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.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]] 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`.
encoding encoding: object 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]] 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.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]] 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
#.9DOZep __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
-------
bool ConvertOptions.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
-------
bool ParseOptions.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
-------
bool ReadOptions.validate(self) self.options cannot be converted to a Python object for pickling no 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 pickling self.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_option ReadOptions.__setstate_cython__ WriteOptions.__reduce_cython__ ParseOptions.__reduce_cython__ Expecting an ASCII character ReadOptions.__reduce_cython__ CSVWriter.__setstate_cython__ quoted_strings_can_be_null autogenerate_column_names auto_dict_max_cardinality _ISO8601.__setstate_cython__ ConvertOptions.__setstate__ ConvertOptions.__getstate__ CSVWriter.__reduce_cython__ include_missing_columns ParseOptions.__setstate__ ParseOptions.__getstate__ _ISO8601.__reduce_cython__ ConvertOptions.validate ReadOptions.__setstate__ ReadOptions.__getstate__ skip_rows_after_names __pyx_unpickle__ISO8601 WriteOptions.validate ParseOptions.validate ConvertOptions.equals ReadOptions.validate write_csv (line 1453) strings_can_be_null invalid_row_handler ParseOptions.equals read_csv (line 1184) newlines_in_values ignore_empty_lines cline_in_traceback asyncio.coroutines ReadOptions.equals CSVStreamingReader timestamp_parsers c_convert_options SignalStopHandler pyarrow/_csv.pyx expected_columns auto_dict_encode __setstate_cython__ quoting_style __pyx_PickleError include_columns exception_class convert_options collections.abc c_write_options c_parse_options " is not a valid include_header c_read_options actual_columns ConvertOptions AssertionError write_options __reduce_cython__ quoting style parse_options decimal_point c_memory_pool use_setstate <stringsource> stop_handler read_options __pyx_checksum pyarrow._csv _is_coroutine false_values double_quote column_types column_names WriteOptions ParseOptions _InvalidRow use_threads true_values pyarrow.lib output_file null_values __mro_entries__ memory_pool escape_char description collections ReadOptions PickleError Do not call stop_token quote_char __pyx_vtable__ __pyx_result namedtuple io_context input_file check_utf8 block_size batch_size ValueError InvalidRow write_csv skip_rows __reduce_ex__ __pyx_state __metaclass__ isenabled frombytes delimiter all_valid TypeError _ISO8601 CSVWriter validate __setstate__ __set_name__ read_csv __qualname__ __pyx_type open_csv __module__ __getstate__ encoding add_note tobytes __prepare__ disable Mapping ISO8601 update stream schema __reduce__ reader pickle number needed __name__ module equals enable _dict value table state __slots__ other items error __enter__ __class__ batch utf8 type text __test__ skip sink self none name __main__ __func__ __exit__ __dict__ data pop __new__ __doc__ gc ? . ' " FN5arrow3csv16InvalidRowResultEP7_objectRKNS0_10InvalidRowEE PFN5arrow3csv16InvalidRowResultEP7_objectRKNS0_10InvalidRowEE @ ;ü (ýÿ ¤ýÿ| H
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