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duality-group
duality-group / pyarrow python
Repository URL to install this package:
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Version:
9.0.0 ▾
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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 %s.%s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject %s() got multiple values for keyword argument '%U' %.200s() keywords must be strings %s() got an unexpected keyword argument '%U' raise: exception class must be a subclass of BaseException calling %R should have returned an instance of BaseException, not %R Missing type object Cannot convert %.200s to %.200s Argument '%.200s' has incorrect type (expected %.200s, got %.200s) name '%U' is not defined cannot import name %S __cinit__ /arrow/python/build/temp.linux-aarch64-3.10/_csv.cpp %s (%s:%d) pyarrow/_csv.pyx pyarrow._csv.ReadOptions.__getstate__ pyarrow._csv.ParseOptions.__getstate__ 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__ __del__ pyarrow._csv.WriteOptions.delimiter.__get__ pyarrow._csv.WriteOptions.batch_size.__get__ pyarrow._csv.ReadOptions.skip_rows_after_names.__get__ pyarrow._csv.ReadOptions.skip_rows.__get__ pyarrow._csv.ReadOptions.block_size.__get__ pyarrow._csv.ParseOptions.invalid_row_handler.__set__ pyarrow._csv.ParseOptions.escape_char.__set__ pyarrow._csv.ParseOptions.quote_char.__set__ pyarrow._csv.ParseOptions.delimiter.__set__ pyarrow._csv.ParseOptions.delimiter.__get__ pyarrow._csv.ConvertOptions.auto_dict_max_cardinality.__get__ pyarrow._csv.ConvertOptions.decimal_point.__set__ pyarrow._csv.ConvertOptions.decimal_point.__get__ stringsource pyarrow._csv.CSVWriter.__setstate_cython__ pyarrow._csv.CSVWriter.__reduce_cython__ pyarrow._csv.WriteOptions.__setstate_cython__ pyarrow._csv.WriteOptions.__reduce_cython__ 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__ pyarrow._csv.ReadOptions.__init__ exactly %.200s() takes %.8s %zd positional argument%.1s (%zd given) Unable to initialize pickling for %s pyarrow.lib _Weakrefable RecordBatchReader _CRecordBatchWriter pyarrow._csv.ParseOptions.__init__ pyarrow._csv.ParseOptions.double_quote.__set__ pyarrow._csv.ConvertOptions.check_utf8.__set__ pyarrow._csv.ReadOptions.use_threads.__set__ pyarrow._csv.ParseOptions.newlines_in_values.__set__ pyarrow._csv.ConvertOptions.auto_dict_encode.__set__ pyarrow._csv.ConvertOptions.quoted_strings_can_be_null.__set__ pyarrow._csv.ConvertOptions.strings_can_be_null.__set__ pyarrow._csv.ConvertOptions.include_missing_columns.__set__ pyarrow._csv.ReadOptions.autogenerate_column_names.__set__ pyarrow._csv.ParseOptions.ignore_empty_lines.__set__ pyarrow._csv.WriteOptions.include_header.__set__ object of type 'NoneType' has no len() hasattr(): attribute name must be string pyarrow._csv.__pyx_unpickle__ISO8601__set_state tuple Expected %.16s, got %.200s pyarrow._csv._ISO8601.__setstate_cython__ string.from_py.__pyx_convert_string_from_py_std__in_string pyarrow._csv.ConvertOptions.__init__ pyarrow._csv._ISO8601.__reduce_cython__ pyarrow._csv.ParseOptions.quote_char.__get__ pyarrow._csv.ParseOptions.escape_char.__get__ pyarrow._csv.ConvertOptions.auto_dict_max_cardinality.__set__ pyarrow._csv.WriteOptions.batch_size.__set__ pyarrow._csv.ReadOptions.block_size.__set__ pyarrow._csv.ReadOptions.skip_rows.__set__ pyarrow._csv.ReadOptions.skip_rows_after_names.__set__ Module '_csv' has already been imported. Re-initialisation is not supported. %d.%d pyarrow._csv compiletime version %s of module '%.100s' does not match runtime version %s builtins cython_runtime __builtins__ type bool complex datetime time timedelta tzinfo IpcWriteOptions IpcReadOptions Message MemoryPool DataType ListType LargeListType MapType FixedSizeListType StructType DictionaryMemo DictionaryType TimestampType Time32Type Time64Type DurationType FixedSizeBinaryType Decimal128Type Decimal256Type BaseExtensionType ExtensionType PyExtensionType _Metadata KeyValueMetadata Field Schema Scalar _PandasConvertible Tensor SparseCSRMatrix SparseCSCMatrix SparseCOOTensor SparseCSFTensor NullArray BooleanArray NumericArray IntegerArray FloatingPointArray Int8Array UInt8Array Int16Array UInt16Array Int32Array UInt32Array Int64Array UInt64Array HalfFloatArray FloatArray DoubleArray FixedSizeBinaryArray Decimal128Array Decimal256Array StructArray BaseListArray ListArray LargeListArray MapArray FixedSizeListArray UnionArray StringArray BinaryArray DictionaryArray ExtensionArray MonthDayNanoIntervalArray ChunkedArray Table RecordBatch Buffer ResizableBuffer NativeFile BufferedInputStream BufferedOutputStream CompressedInputStream CompressedOutputStream Codec StopToken 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 metaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its bases init pyarrow._csv pyarrow._csv.ConvertOptions.__eq__ pyarrow._csv.ReadOptions.__eq__ pyarrow._csv.ParseOptions.__eq__ pyarrow._csv.ConvertOptions.__getstate__ __pyx_unpickle__ISO8601 pyarrow._csv.__pyx_unpickle__ISO8601 pyarrow._csv.CSVStreamingReader.__init__ pyarrow._csv.ParseOptions.wrap too many values to unpack (expected %zd) need more than %zd value%.1s to unpack pyarrow._csv.ReadOptions.__setstate__ pyarrow._csv.ParseOptions.__setstate__ pyarrow._csv.WriteOptions.__init__ pyarrow._csv.ReadOptions.wrap other pyarrow._csv.ConvertOptions.equals pyarrow._csv.WriteOptions.wrap string.to_py.__pyx_convert_PyBytes_string_to_py_std__in_string pyarrow._csv.ConvertOptions.null_values.__get__ pyarrow._csv.ConvertOptions.include_columns.__get__ pyarrow._csv.ConvertOptions.false_values.__get__ pyarrow._csv.ConvertOptions.true_values.__get__ pyarrow._csv.ReadOptions.column_names.__get__ pyarrow._csv.ParseOptions.equals pyarrow._csv.ReadOptions.equals pyarrow._csv.ConvertOptions.__setstate__ basic_string::_S_construct null not valid pyarrow._csv._handle_invalid_row pyarrow._csv.ConvertOptions.wrap pyarrow._csv.ConvertOptions.column_types.__get__ strptime iso8601 pyarrow._csv.ConvertOptions.timestamp_parsers.__get__ pyarrow._csv.ReadOptions.validate pyarrow._csv.ConvertOptions.validate pyarrow._csv.WriteOptions.validate pyarrow._csv.ParseOptions.validate pyarrow._csv._get_reader at most at least write_csv pyarrow._csv.write_csv memory_pool join() result is too long for a Python string pyarrow._csv.CSVStreamingReader._open pyarrow._csv.CSVWriter.__init__ schema write_options pyarrow._csv.ReadOptions.column_names.__set__ pyarrow._csv.ConvertOptions.include_columns.__set__ pyarrow._csv.ConvertOptions.timestamp_parsers.__set__ pyarrow._csv.open_csv open_csv pyarrow._csv.read_csv read_csv vector.from_py.__pyx_convert_vector_from_py_std_3a__3a_string pyarrow._csv.ConvertOptions.false_values.__set__ pyarrow._csv.ConvertOptions.true_values.__set__ pyarrow._csv.ConvertOptions.null_values.__set__ pyarrow._csv.ConvertOptions.column_types.__set__ 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)
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.
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.
validate pyarrow._csv.ReadOptions ReadOptions(use_threads=None, *, block_size=None, skip_rows=None, column_names=None, autogenerate_column_names=None, encoding=u'utf8', skip_rows_after_names=None)
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 aread (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
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`.
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 aread (unless `column_names` is set);
- `skip_rows_after_names` is applied (if non-zero).
encoding encoding: object equals __getstate__ __setstate__ 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\nFlamingo;2;2022-03-01\n# Comment here:\nHorse;4;2022-03-02\nBrittle stars;5;2022-03-03\nCentipede;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.
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.
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.
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.
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.
Examples
--------
Defining an example data:
>>> import io
>>> s = "animals,n_legs,entry,fast\nFlamingo,2,01/03/2022,Yes\nHorse,4,02/03/2022,Yes\nBrittle stars,5,03/03/2022,No\nCentipede,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 FN5arrow3csv16InvalidRowResultEP7_objectRKNS0_10InvalidRowEE PFN5arrow3csv16InvalidRowResultEP7_objectRKNS0_10InvalidRowEE @ self.options cannot be converted to a Python object for pickling no default __reduce__ due to non-trivial __cinit__ Invalid return value for invalid row handler: expected 'error' or 'skip', got Incompatible checksums (0x%x vs (0xd41d8cd, 0xe3b0c44, 0xda39a3e) = ()) Expected callable or None, got instance of Expected Table or RecordBatch, got ' Do not call {}'s constructor directly, use pyarrow.csv.open_csv() instead. self.writer cannot be converted to a Python object for pickling self.reader cannot be converted to a Python object for pickling
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\nFlamingo,2,2022-03-01\nHorse,4,2022-03-02\nBrittle stars,5,2022-03-03\nCentipede,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)
Expecting an ASCII character quoted_strings_can_be_null autogenerate_column_names auto_dict_max_cardinality include_missing_columns skip_rows_after_names __pyx_unpickle__ISO8601 write_csv (line 1357) strings_can_be_null invalid_row_handler read_csv (line 1147) newlines_in_values ignore_empty_lines cline_in_traceback CSVStreamingReader timestamp_parsers c_convert_options SignalStopHandler pyarrow/_csv.pyx expected_columns auto_dict_encode __setstate_cython__ __pyx_PickleError include_columns convert_options collections.abc c_write_options c_parse_options _stringify_path include_header c_read_options actual_columns ConvertOptions write_options __reduce_cython__ parse_options decimal_point c_memory_pool stringsource stop_handler read_options __pyx_checksum pyarrow.util pyarrow._csv false_values double_quote column_types column_names WriteOptions ParseOptions _InvalidRow use_threads true_values pyarrow.lib output_file null_values memory_pool escape_char collections ReadOptions PickleError 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__ frombytes delimiter TypeError _ISO8601 CSVWriter __setstate__ read_csv __qualname__ __pyx_type open_csv __module__ __getstate__ encoding tobytes __prepare__ Mapping ISO8601 update stream schema __reduce__ reader pickle number __name__ module __import__ format equals codecs table __slots__ items error __enter__ __class__ batch utf8 type text __test__ skip sink name __main__ __exit__ __dict__ data __new__ __doc__ chr ' ;È Ø Ü7ýÿð ¼:ýÿ P>ýÿT
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w èÿ Ø } = @ þÿÿo < ÿÿÿo ðÿÿo : ùÿÿo n `ü ÿÿÿÿÿÿÿÿ __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) 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 Table 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\nFlamingo,2,2022-03-01\nHorse,4,2022-03-02\nBrittle stars,5,2022-03-03\nCentipede,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]]
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