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keshe/.venv/Lib/site-packages/pandas/io/parsers/readers.py

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"""
Module contains tools for processing files into DataFrames or other objects
GH#48849 provides a convenient way of deprecating keyword arguments
"""
from __future__ import annotations
from collections import (
abc,
defaultdict,
)
import csv
import sys
from textwrap import fill
from typing import (
IO,
TYPE_CHECKING,
Any,
Callable,
Literal,
NamedTuple,
TypedDict,
overload,
)
import warnings
import numpy as np
from pandas._config import using_copy_on_write
from pandas._libs import lib
from pandas._libs.parsers import STR_NA_VALUES
from pandas.errors import (
AbstractMethodError,
ParserWarning,
)
from pandas.util._decorators import Appender
from pandas.util._exceptions import find_stack_level
from pandas.util._validators import check_dtype_backend
from pandas.core.dtypes.common import (
is_file_like,
is_float,
is_hashable,
is_integer,
is_list_like,
pandas_dtype,
)
from pandas import Series
from pandas.core.frame import DataFrame
from pandas.core.indexes.api import RangeIndex
from pandas.core.shared_docs import _shared_docs
from pandas.io.common import (
IOHandles,
get_handle,
stringify_path,
validate_header_arg,
)
from pandas.io.parsers.arrow_parser_wrapper import ArrowParserWrapper
from pandas.io.parsers.base_parser import (
ParserBase,
is_index_col,
parser_defaults,
)
from pandas.io.parsers.c_parser_wrapper import CParserWrapper
from pandas.io.parsers.python_parser import (
FixedWidthFieldParser,
PythonParser,
)
if TYPE_CHECKING:
from collections.abc import (
Hashable,
Iterable,
Mapping,
Sequence,
)
from types import TracebackType
from pandas._typing import (
CompressionOptions,
CSVEngine,
DtypeArg,
DtypeBackend,
FilePath,
IndexLabel,
ReadCsvBuffer,
Self,
StorageOptions,
UsecolsArgType,
)
_doc_read_csv_and_table = (
r"""
{summary}
Also supports optionally iterating or breaking of the file
into chunks.
Additional help can be found in the online docs for
`IO Tools <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_.
Parameters
----------
filepath_or_buffer : str, path object or file-like object
Any valid string path is acceptable. The string could be a URL. Valid
URL schemes include http, ftp, s3, gs, and file. For file URLs, a host is
expected. A local file could be: file://localhost/path/to/table.csv.
If you want to pass in a path object, pandas accepts any ``os.PathLike``.
By file-like object, we refer to objects with a ``read()`` method, such as
a file handle (e.g. via builtin ``open`` function) or ``StringIO``.
sep : str, default {_default_sep}
Character or regex pattern to treat as the delimiter. If ``sep=None``, the
C engine cannot automatically detect
the separator, but the Python parsing engine can, meaning the latter will
be used and automatically detect the separator from only the first valid
row of the file by Python's builtin sniffer tool, ``csv.Sniffer``.
In addition, separators longer than 1 character and different from
``'\s+'`` will be interpreted as regular expressions and will also force
the use of the Python parsing engine. Note that regex delimiters are prone
to ignoring quoted data. Regex example: ``'\r\t'``.
delimiter : str, optional
Alias for ``sep``.
header : int, Sequence of int, 'infer' or None, default 'infer'
Row number(s) containing column labels and marking the start of the
data (zero-indexed). Default behavior is to infer the column names: if no ``names``
are passed the behavior is identical to ``header=0`` and column
names are inferred from the first line of the file, if column
names are passed explicitly to ``names`` then the behavior is identical to
``header=None``. Explicitly pass ``header=0`` to be able to
replace existing names. The header can be a list of integers that
specify row locations for a :class:`~pandas.MultiIndex` on the columns
e.g. ``[0, 1, 3]``. Intervening rows that are not specified will be
skipped (e.g. 2 in this example is skipped). Note that this
parameter ignores commented lines and empty lines if
``skip_blank_lines=True``, so ``header=0`` denotes the first line of
data rather than the first line of the file.
names : Sequence of Hashable, optional
Sequence of column labels to apply. If the file contains a header row,
then you should explicitly pass ``header=0`` to override the column names.
Duplicates in this list are not allowed.
index_col : Hashable, Sequence of Hashable or False, optional
Column(s) to use as row label(s), denoted either by column labels or column
indices. If a sequence of labels or indices is given, :class:`~pandas.MultiIndex`
will be formed for the row labels.
Note: ``index_col=False`` can be used to force pandas to *not* use the first
column as the index, e.g., when you have a malformed file with delimiters at
the end of each line.
usecols : Sequence of Hashable or Callable, optional
Subset of columns to select, denoted either by column labels or column indices.
If list-like, all elements must either
be positional (i.e. integer indices into the document columns) or strings
that correspond to column names provided either by the user in ``names`` or
inferred from the document header row(s). If ``names`` are given, the document
header row(s) are not taken into account. For example, a valid list-like
``usecols`` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``.
Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.
To instantiate a :class:`~pandas.DataFrame` from ``data`` with element order
preserved use ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]``
for columns in ``['foo', 'bar']`` order or
``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``
for ``['bar', 'foo']`` order.
If callable, the callable function will be evaluated against the column
names, returning names where the callable function evaluates to ``True``. An
example of a valid callable argument would be ``lambda x: x.upper() in
['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster
parsing time and lower memory usage.
dtype : dtype or dict of {{Hashable : dtype}}, optional
Data type(s) to apply to either the whole dataset or individual columns.
E.g., ``{{'a': np.float64, 'b': np.int32, 'c': 'Int64'}}``
Use ``str`` or ``object`` together with suitable ``na_values`` settings
to preserve and not interpret ``dtype``.
If ``converters`` are specified, they will be applied INSTEAD
of ``dtype`` conversion.
.. versionadded:: 1.5.0
Support for ``defaultdict`` was added. Specify a ``defaultdict`` as input where
the default determines the ``dtype`` of the columns which are not explicitly
listed.
engine : {{'c', 'python', 'pyarrow'}}, optional
Parser engine to use. The C and pyarrow engines are faster, while the python engine
is currently more feature-complete. Multithreading is currently only supported by
the pyarrow engine.
.. versionadded:: 1.4.0
The 'pyarrow' engine was added as an *experimental* engine, and some features
are unsupported, or may not work correctly, with this engine.
converters : dict of {{Hashable : Callable}}, optional
Functions for converting values in specified columns. Keys can either
be column labels or column indices.
true_values : list, optional
Values to consider as ``True`` in addition to case-insensitive variants of 'True'.
false_values : list, optional
Values to consider as ``False`` in addition to case-insensitive variants of 'False'.
skipinitialspace : bool, default False
Skip spaces after delimiter.
skiprows : int, list of int or Callable, optional
Line numbers to skip (0-indexed) or number of lines to skip (``int``)
at the start of the file.
If callable, the callable function will be evaluated against the row
indices, returning ``True`` if the row should be skipped and ``False`` otherwise.
An example of a valid callable argument would be ``lambda x: x in [0, 2]``.
skipfooter : int, default 0
Number of lines at bottom of file to skip (Unsupported with ``engine='c'``).
nrows : int, optional
Number of rows of file to read. Useful for reading pieces of large files.
na_values : Hashable, Iterable of Hashable or dict of {{Hashable : Iterable}}, optional
Additional strings to recognize as ``NA``/``NaN``. If ``dict`` passed, specific
per-column ``NA`` values. By default the following values are interpreted as
``NaN``: " """
+ fill('", "'.join(sorted(STR_NA_VALUES)), 70, subsequent_indent=" ")
+ """ ".
keep_default_na : bool, default True
Whether or not to include the default ``NaN`` values when parsing the data.
Depending on whether ``na_values`` is passed in, the behavior is as follows:
* If ``keep_default_na`` is ``True``, and ``na_values`` are specified, ``na_values``
is appended to the default ``NaN`` values used for parsing.
* If ``keep_default_na`` is ``True``, and ``na_values`` are not specified, only
the default ``NaN`` values are used for parsing.
* If ``keep_default_na`` is ``False``, and ``na_values`` are specified, only
the ``NaN`` values specified ``na_values`` are used for parsing.
* If ``keep_default_na`` is ``False``, and ``na_values`` are not specified, no
strings will be parsed as ``NaN``.
Note that if ``na_filter`` is passed in as ``False``, the ``keep_default_na`` and
``na_values`` parameters will be ignored.
na_filter : bool, default True
Detect missing value markers (empty strings and the value of ``na_values``). In
data without any ``NA`` values, passing ``na_filter=False`` can improve the
performance of reading a large file.
verbose : bool, default False
Indicate number of ``NA`` values placed in non-numeric columns.
.. deprecated:: 2.2.0
skip_blank_lines : bool, default True
If ``True``, skip over blank lines rather than interpreting as ``NaN`` values.
parse_dates : bool, list of Hashable, list of lists or dict of {{Hashable : list}}, \
default False
The behavior is as follows:
* ``bool``. If ``True`` -> try parsing the index. Note: Automatically set to
``True`` if ``date_format`` or ``date_parser`` arguments have been passed.
* ``list`` of ``int`` or names. e.g. If ``[1, 2, 3]`` -> try parsing columns 1, 2, 3
each as a separate date column.
* ``list`` of ``list``. e.g. If ``[[1, 3]]`` -> combine columns 1 and 3 and parse
as a single date column. Values are joined with a space before parsing.
* ``dict``, e.g. ``{{'foo' : [1, 3]}}`` -> parse columns 1, 3 as date and call
result 'foo'. Values are joined with a space before parsing.
If a column or index cannot be represented as an array of ``datetime``,
say because of an unparsable value or a mixture of timezones, the column
or index will be returned unaltered as an ``object`` data type. For
non-standard ``datetime`` parsing, use :func:`~pandas.to_datetime` after
:func:`~pandas.read_csv`.
Note: A fast-path exists for iso8601-formatted dates.
infer_datetime_format : bool, default False
If ``True`` and ``parse_dates`` is enabled, pandas will attempt to infer the
format of the ``datetime`` strings in the columns, and if it can be inferred,
switch to a faster method of parsing them. In some cases this can increase
the parsing speed by 5-10x.
.. deprecated:: 2.0.0
A strict version of this argument is now the default, passing it has no effect.
keep_date_col : bool, default False
If ``True`` and ``parse_dates`` specifies combining multiple columns then
keep the original columns.
date_parser : Callable, optional
Function to use for converting a sequence of string columns to an array of
``datetime`` instances. The default uses ``dateutil.parser.parser`` to do the
conversion. pandas will try to call ``date_parser`` in three different ways,
advancing to the next if an exception occurs: 1) Pass one or more arrays
(as defined by ``parse_dates``) as arguments; 2) concatenate (row-wise) the
string values from the columns defined by ``parse_dates`` into a single array
and pass that; and 3) call ``date_parser`` once for each row using one or
more strings (corresponding to the columns defined by ``parse_dates``) as
arguments.
.. deprecated:: 2.0.0
Use ``date_format`` instead, or read in as ``object`` and then apply
:func:`~pandas.to_datetime` as-needed.
date_format : str or dict of column -> format, optional
Format to use for parsing dates when used in conjunction with ``parse_dates``.
The strftime to parse time, e.g. :const:`"%d/%m/%Y"`. See
`strftime documentation
<https://docs.python.org/3/library/datetime.html
#strftime-and-strptime-behavior>`_ for more information on choices, though
note that :const:`"%f"` will parse all the way up to nanoseconds.
You can also pass:
- "ISO8601", to parse any `ISO8601 <https://en.wikipedia.org/wiki/ISO_8601>`_
time string (not necessarily in exactly the same format);
- "mixed", to infer the format for each element individually. This is risky,
and you should probably use it along with `dayfirst`.
.. versionadded:: 2.0.0
dayfirst : bool, default False
DD/MM format dates, international and European format.
cache_dates : bool, default True
If ``True``, use a cache of unique, converted dates to apply the ``datetime``
conversion. May produce significant speed-up when parsing duplicate
date strings, especially ones with timezone offsets.
iterator : bool, default False
Return ``TextFileReader`` object for iteration or getting chunks with
``get_chunk()``.
chunksize : int, optional
Number of lines to read from the file per chunk. Passing a value will cause the
function to return a ``TextFileReader`` object for iteration.
See the `IO Tools docs
<https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_
for more information on ``iterator`` and ``chunksize``.
{decompression_options}
.. versionchanged:: 1.4.0 Zstandard support.
thousands : str (length 1), optional
Character acting as the thousands separator in numerical values.
decimal : str (length 1), default '.'
Character to recognize as decimal point (e.g., use ',' for European data).
lineterminator : str (length 1), optional
Character used to denote a line break. Only valid with C parser.
quotechar : str (length 1), optional
Character used to denote the start and end of a quoted item. Quoted
items can include the ``delimiter`` and it will be ignored.
quoting : {{0 or csv.QUOTE_MINIMAL, 1 or csv.QUOTE_ALL, 2 or csv.QUOTE_NONNUMERIC, \
3 or csv.QUOTE_NONE}}, default csv.QUOTE_MINIMAL
Control field quoting behavior per ``csv.QUOTE_*`` constants. Default is
``csv.QUOTE_MINIMAL`` (i.e., 0) which implies that only fields containing special
characters are quoted (e.g., characters defined in ``quotechar``, ``delimiter``,
or ``lineterminator``.
doublequote : bool, default True
When ``quotechar`` is specified and ``quoting`` is not ``QUOTE_NONE``, indicate
whether or not to interpret two consecutive ``quotechar`` elements INSIDE a
field as a single ``quotechar`` element.
escapechar : str (length 1), optional
Character used to escape other characters.
comment : str (length 1), optional
Character indicating that the remainder of line should not be parsed.
If found at the beginning
of a line, the line will be ignored altogether. This parameter must be a
single character. Like empty lines (as long as ``skip_blank_lines=True``),
fully commented lines are ignored by the parameter ``header`` but not by
``skiprows``. For example, if ``comment='#'``, parsing
``#empty\\na,b,c\\n1,2,3`` with ``header=0`` will result in ``'a,b,c'`` being
treated as the header.
encoding : str, optional, default 'utf-8'
Encoding to use for UTF when reading/writing (ex. ``'utf-8'``). `List of Python
standard encodings
<https://docs.python.org/3/library/codecs.html#standard-encodings>`_ .
encoding_errors : str, optional, default 'strict'
How encoding errors are treated. `List of possible values
<https://docs.python.org/3/library/codecs.html#error-handlers>`_ .
.. versionadded:: 1.3.0
dialect : str or csv.Dialect, optional
If provided, this parameter will override values (default or not) for the
following parameters: ``delimiter``, ``doublequote``, ``escapechar``,
``skipinitialspace``, ``quotechar``, and ``quoting``. If it is necessary to
override values, a ``ParserWarning`` will be issued. See ``csv.Dialect``
documentation for more details.
on_bad_lines : {{'error', 'warn', 'skip'}} or Callable, default 'error'
Specifies what to do upon encountering a bad line (a line with too many fields).
Allowed values are :
- ``'error'``, raise an Exception when a bad line is encountered.
- ``'warn'``, raise a warning when a bad line is encountered and skip that line.
- ``'skip'``, skip bad lines without raising or warning when they are encountered.
.. versionadded:: 1.3.0
.. versionadded:: 1.4.0
- Callable, function with signature
``(bad_line: list[str]) -> list[str] | None`` that will process a single
bad line. ``bad_line`` is a list of strings split by the ``sep``.
If the function returns ``None``, the bad line will be ignored.
If the function returns a new ``list`` of strings with more elements than
expected, a ``ParserWarning`` will be emitted while dropping extra elements.
Only supported when ``engine='python'``
.. versionchanged:: 2.2.0
- Callable, function with signature
as described in `pyarrow documentation
<https://arrow.apache.org/docs/python/generated/pyarrow.csv.ParseOptions.html
#pyarrow.csv.ParseOptions.invalid_row_handler>`_ when ``engine='pyarrow'``
delim_whitespace : bool, default False
Specifies whether or not whitespace (e.g. ``' '`` or ``'\\t'``) will be
used as the ``sep`` delimiter. Equivalent to setting ``sep='\\s+'``. If this option
is set to ``True``, nothing should be passed in for the ``delimiter``
parameter.
.. deprecated:: 2.2.0
Use ``sep="\\s+"`` instead.
low_memory : bool, default True
Internally process the file in chunks, resulting in lower memory use
while parsing, but possibly mixed type inference. To ensure no mixed
types either set ``False``, or specify the type with the ``dtype`` parameter.
Note that the entire file is read into a single :class:`~pandas.DataFrame`
regardless, use the ``chunksize`` or ``iterator`` parameter to return the data in
chunks. (Only valid with C parser).
memory_map : bool, default False
If a filepath is provided for ``filepath_or_buffer``, map the file object
directly onto memory and access the data directly from there. Using this
option can improve performance because there is no longer any I/O overhead.
float_precision : {{'high', 'legacy', 'round_trip'}}, optional
Specifies which converter the C engine should use for floating-point
values. The options are ``None`` or ``'high'`` for the ordinary converter,
``'legacy'`` for the original lower precision pandas converter, and
``'round_trip'`` for the round-trip converter.
{storage_options}
dtype_backend : {{'numpy_nullable', 'pyarrow'}}, default 'numpy_nullable'
Back-end data type applied to the resultant :class:`DataFrame`
(still experimental). Behaviour is as follows:
* ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
(default).
* ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
DataFrame.
.. versionadded:: 2.0
Returns
-------
DataFrame or TextFileReader
A comma-separated values (csv) file is returned as two-dimensional
data structure with labeled axes.
See Also
--------
DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
{see_also_func_name} : {see_also_func_summary}
read_fwf : Read a table of fixed-width formatted lines into DataFrame.
Examples
--------
>>> pd.{func_name}('data.csv') # doctest: +SKIP
"""
)
class _C_Parser_Defaults(TypedDict):
delim_whitespace: Literal[False]
na_filter: Literal[True]
low_memory: Literal[True]
memory_map: Literal[False]
float_precision: None
_c_parser_defaults: _C_Parser_Defaults = {
"delim_whitespace": False,
"na_filter": True,
"low_memory": True,
"memory_map": False,
"float_precision": None,
}
class _Fwf_Defaults(TypedDict):
colspecs: Literal["infer"]
infer_nrows: Literal[100]
widths: None
_fwf_defaults: _Fwf_Defaults = {"colspecs": "infer", "infer_nrows": 100, "widths": None}
_c_unsupported = {"skipfooter"}
_python_unsupported = {"low_memory", "float_precision"}
_pyarrow_unsupported = {
"skipfooter",
"float_precision",
"chunksize",
"comment",
"nrows",
"thousands",
"memory_map",
"dialect",
"delim_whitespace",
"quoting",
"lineterminator",
"converters",
"iterator",
"dayfirst",
"verbose",
"skipinitialspace",
"low_memory",
}
class _DeprecationConfig(NamedTuple):
default_value: Any
msg: str | None
@overload
def validate_integer(name: str, val: None, min_val: int = ...) -> None:
...
@overload
def validate_integer(name: str, val: float, min_val: int = ...) -> int:
...
@overload
def validate_integer(name: str, val: int | None, min_val: int = ...) -> int | None:
...
def validate_integer(
name: str, val: int | float | None, min_val: int = 0
) -> int | None:
"""
Checks whether the 'name' parameter for parsing is either
an integer OR float that can SAFELY be cast to an integer
without losing accuracy. Raises a ValueError if that is
not the case.
Parameters
----------
name : str
Parameter name (used for error reporting)
val : int or float
The value to check
min_val : int
Minimum allowed value (val < min_val will result in a ValueError)
"""
if val is None:
return val
msg = f"'{name:s}' must be an integer >={min_val:d}"
if is_float(val):
if int(val) != val:
raise ValueError(msg)
val = int(val)
elif not (is_integer(val) and val >= min_val):
raise ValueError(msg)
return int(val)
def _validate_names(names: Sequence[Hashable] | None) -> None:
"""
Raise ValueError if the `names` parameter contains duplicates or has an
invalid data type.
Parameters
----------
names : array-like or None
An array containing a list of the names used for the output DataFrame.
Raises
------
ValueError
If names are not unique or are not ordered (e.g. set).
"""
if names is not None:
if len(names) != len(set(names)):
raise ValueError("Duplicate names are not allowed.")
if not (
is_list_like(names, allow_sets=False) or isinstance(names, abc.KeysView)
):
raise ValueError("Names should be an ordered collection.")
def _read(
filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str], kwds
) -> DataFrame | TextFileReader:
"""Generic reader of line files."""
# if we pass a date_parser and parse_dates=False, we should not parse the
# dates GH#44366
if kwds.get("parse_dates", None) is None:
if (
kwds.get("date_parser", lib.no_default) is lib.no_default
and kwds.get("date_format", None) is None
):
kwds["parse_dates"] = False
else:
kwds["parse_dates"] = True
# Extract some of the arguments (pass chunksize on).
iterator = kwds.get("iterator", False)
chunksize = kwds.get("chunksize", None)
if kwds.get("engine") == "pyarrow":
if iterator:
raise ValueError(
"The 'iterator' option is not supported with the 'pyarrow' engine"
)
if chunksize is not None:
raise ValueError(
"The 'chunksize' option is not supported with the 'pyarrow' engine"
)
else:
chunksize = validate_integer("chunksize", chunksize, 1)
nrows = kwds.get("nrows", None)
# Check for duplicates in names.
_validate_names(kwds.get("names", None))
# Create the parser.
parser = TextFileReader(filepath_or_buffer, **kwds)
if chunksize or iterator:
return parser
with parser:
return parser.read(nrows)
# iterator=True -> TextFileReader
@overload
def read_csv(
filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
*,
sep: str | None | lib.NoDefault = ...,
delimiter: str | None | lib.NoDefault = ...,
header: int | Sequence[int] | None | Literal["infer"] = ...,
names: Sequence[Hashable] | None | lib.NoDefault = ...,
index_col: IndexLabel | Literal[False] | None = ...,
usecols: UsecolsArgType = ...,
dtype: DtypeArg | None = ...,
engine: CSVEngine | None = ...,
converters: Mapping[Hashable, Callable] | None = ...,
true_values: list | None = ...,
false_values: list | None = ...,
skipinitialspace: bool = ...,
skiprows: list[int] | int | Callable[[Hashable], bool] | None = ...,
skipfooter: int = ...,
nrows: int | None = ...,
na_values: Hashable
| Iterable[Hashable]
| Mapping[Hashable, Iterable[Hashable]]
| None = ...,
na_filter: bool = ...,
verbose: bool | lib.NoDefault = ...,
skip_blank_lines: bool = ...,
parse_dates: bool | Sequence[Hashable] | None = ...,
infer_datetime_format: bool | lib.NoDefault = ...,
keep_date_col: bool | lib.NoDefault = ...,
date_parser: Callable | lib.NoDefault = ...,
date_format: str | dict[Hashable, str] | None = ...,
dayfirst: bool = ...,
cache_dates: bool = ...,
iterator: Literal[True],
chunksize: int | None = ...,
compression: CompressionOptions = ...,
thousands: str | None = ...,
decimal: str = ...,
lineterminator: str | None = ...,
quotechar: str = ...,
quoting: int = ...,
doublequote: bool = ...,
escapechar: str | None = ...,
comment: str | None = ...,
encoding: str | None = ...,
encoding_errors: str | None = ...,
dialect: str | csv.Dialect | None = ...,
on_bad_lines=...,
delim_whitespace: bool | lib.NoDefault = ...,
low_memory: bool = ...,
memory_map: bool = ...,
float_precision: Literal["high", "legacy"] | None = ...,
storage_options: StorageOptions = ...,
dtype_backend: DtypeBackend | lib.NoDefault = ...,
) -> TextFileReader:
...
# chunksize=int -> TextFileReader
@overload
def read_csv(
filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
*,
sep: str | None | lib.NoDefault = ...,
delimiter: str | None | lib.NoDefault = ...,
header: int | Sequence[int] | None | Literal["infer"] = ...,
names: Sequence[Hashable] | None | lib.NoDefault = ...,
index_col: IndexLabel | Literal[False] | None = ...,
usecols: UsecolsArgType = ...,
dtype: DtypeArg | None = ...,
engine: CSVEngine | None = ...,
converters: Mapping[Hashable, Callable] | None = ...,
true_values: list | None = ...,
false_values: list | None = ...,
skipinitialspace: bool = ...,
skiprows: list[int] | int | Callable[[Hashable], bool] | None = ...,
skipfooter: int = ...,
nrows: int | None = ...,
na_values: Hashable
| Iterable[Hashable]
| Mapping[Hashable, Iterable[Hashable]]
| None = ...,
keep_default_na: bool = ...,
na_filter: bool = ...,
verbose: bool | lib.NoDefault = ...,
skip_blank_lines: bool = ...,
parse_dates: bool | Sequence[Hashable] | None = ...,
infer_datetime_format: bool | lib.NoDefault = ...,
keep_date_col: bool | lib.NoDefault = ...,
date_parser: Callable | lib.NoDefault = ...,
date_format: str | dict[Hashable, str] | None = ...,
dayfirst: bool = ...,
cache_dates: bool = ...,
iterator: bool = ...,
chunksize: int,
compression: CompressionOptions = ...,
thousands: str | None = ...,
decimal: str = ...,
lineterminator: str | None = ...,
quotechar: str = ...,
quoting: int = ...,
doublequote: bool = ...,
escapechar: str | None = ...,
comment: str | None = ...,
encoding: str | None = ...,
encoding_errors: str | None = ...,
dialect: str | csv.Dialect | None = ...,
on_bad_lines=...,
delim_whitespace: bool | lib.NoDefault = ...,
low_memory: bool = ...,
memory_map: bool = ...,
float_precision: Literal["high", "legacy"] | None = ...,
storage_options: StorageOptions = ...,
dtype_backend: DtypeBackend | lib.NoDefault = ...,
) -> TextFileReader:
...
# default case -> DataFrame
@overload
def read_csv(
filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
*,
sep: str | None | lib.NoDefault = ...,
delimiter: str | None | lib.NoDefault = ...,
header: int | Sequence[int] | None | Literal["infer"] = ...,
names: Sequence[Hashable] | None | lib.NoDefault = ...,
index_col: IndexLabel | Literal[False] | None = ...,
usecols: UsecolsArgType = ...,
dtype: DtypeArg | None = ...,
engine: CSVEngine | None = ...,
converters: Mapping[Hashable, Callable] | None = ...,
true_values: list | None = ...,
false_values: list | None = ...,
skipinitialspace: bool = ...,
skiprows: list[int] | int | Callable[[Hashable], bool] | None = ...,
skipfooter: int = ...,
nrows: int | None = ...,
na_values: Hashable
| Iterable[Hashable]
| Mapping[Hashable, Iterable[Hashable]]
| None = ...,
keep_default_na: bool = ...,
na_filter: bool = ...,
verbose: bool | lib.NoDefault = ...,
skip_blank_lines: bool = ...,
parse_dates: bool | Sequence[Hashable] | None = ...,
infer_datetime_format: bool | lib.NoDefault = ...,
keep_date_col: bool | lib.NoDefault = ...,
date_parser: Callable | lib.NoDefault = ...,
date_format: str | dict[Hashable, str] | None = ...,
dayfirst: bool = ...,
cache_dates: bool = ...,
iterator: Literal[False] = ...,
chunksize: None = ...,
compression: CompressionOptions = ...,
thousands: str | None = ...,
decimal: str = ...,
lineterminator: str | None = ...,
quotechar: str = ...,
quoting: int = ...,
doublequote: bool = ...,
escapechar: str | None = ...,
comment: str | None = ...,
encoding: str | None = ...,
encoding_errors: str | None = ...,
dialect: str | csv.Dialect | None = ...,
on_bad_lines=...,
delim_whitespace: bool | lib.NoDefault = ...,
low_memory: bool = ...,
memory_map: bool = ...,
float_precision: Literal["high", "legacy"] | None = ...,
storage_options: StorageOptions = ...,
dtype_backend: DtypeBackend | lib.NoDefault = ...,
) -> DataFrame:
...
# Unions -> DataFrame | TextFileReader
@overload
def read_csv(
filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
*,
sep: str | None | lib.NoDefault = ...,
delimiter: str | None | lib.NoDefault = ...,
header: int | Sequence[int] | None | Literal["infer"] = ...,
names: Sequence[Hashable] | None | lib.NoDefault = ...,
index_col: IndexLabel | Literal[False] | None = ...,
usecols: UsecolsArgType = ...,
dtype: DtypeArg | None = ...,
engine: CSVEngine | None = ...,
converters: Mapping[Hashable, Callable] | None = ...,
true_values: list | None = ...,
false_values: list | None = ...,
skipinitialspace: bool = ...,
skiprows: list[int] | int | Callable[[Hashable], bool] | None = ...,
skipfooter: int = ...,
nrows: int | None = ...,
na_values: Hashable
| Iterable[Hashable]
| Mapping[Hashable, Iterable[Hashable]]
| None = ...,
keep_default_na: bool = ...,
na_filter: bool = ...,
verbose: bool | lib.NoDefault = ...,
skip_blank_lines: bool = ...,
parse_dates: bool | Sequence[Hashable] | None = ...,
infer_datetime_format: bool | lib.NoDefault = ...,
keep_date_col: bool | lib.NoDefault = ...,
date_parser: Callable | lib.NoDefault = ...,
date_format: str | dict[Hashable, str] | None = ...,
dayfirst: bool = ...,
cache_dates: bool = ...,
iterator: bool = ...,
chunksize: int | None = ...,
compression: CompressionOptions = ...,
thousands: str | None = ...,
decimal: str = ...,
lineterminator: str | None = ...,
quotechar: str = ...,
quoting: int = ...,
doublequote: bool = ...,
escapechar: str | None = ...,
comment: str | None = ...,
encoding: str | None = ...,
encoding_errors: str | None = ...,
dialect: str | csv.Dialect | None = ...,
on_bad_lines=...,
delim_whitespace: bool | lib.NoDefault = ...,
low_memory: bool = ...,
memory_map: bool = ...,
float_precision: Literal["high", "legacy"] | None = ...,
storage_options: StorageOptions = ...,
dtype_backend: DtypeBackend | lib.NoDefault = ...,
) -> DataFrame | TextFileReader:
...
@Appender(
_doc_read_csv_and_table.format(
func_name="read_csv",
summary="Read a comma-separated values (csv) file into DataFrame.",
see_also_func_name="read_table",
see_also_func_summary="Read general delimited file into DataFrame.",
_default_sep="','",
storage_options=_shared_docs["storage_options"],
decompression_options=_shared_docs["decompression_options"]
% "filepath_or_buffer",
)
)
def read_csv(
filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
*,
sep: str | None | lib.NoDefault = lib.no_default,
delimiter: str | None | lib.NoDefault = None,
# Column and Index Locations and Names
header: int | Sequence[int] | None | Literal["infer"] = "infer",
names: Sequence[Hashable] | None | lib.NoDefault = lib.no_default,
index_col: IndexLabel | Literal[False] | None = None,
usecols: UsecolsArgType = None,
# General Parsing Configuration
dtype: DtypeArg | None = None,
engine: CSVEngine | None = None,
converters: Mapping[Hashable, Callable] | None = None,
true_values: list | None = None,
false_values: list | None = None,
skipinitialspace: bool = False,
skiprows: list[int] | int | Callable[[Hashable], bool] | None = None,
skipfooter: int = 0,
nrows: int | None = None,
# NA and Missing Data Handling
na_values: Hashable
| Iterable[Hashable]
| Mapping[Hashable, Iterable[Hashable]]
| None = None,
keep_default_na: bool = True,
na_filter: bool = True,
verbose: bool | lib.NoDefault = lib.no_default,
skip_blank_lines: bool = True,
# Datetime Handling
parse_dates: bool | Sequence[Hashable] | None = None,
infer_datetime_format: bool | lib.NoDefault = lib.no_default,
keep_date_col: bool | lib.NoDefault = lib.no_default,
date_parser: Callable | lib.NoDefault = lib.no_default,
date_format: str | dict[Hashable, str] | None = None,
dayfirst: bool = False,
cache_dates: bool = True,
# Iteration
iterator: bool = False,
chunksize: int | None = None,
# Quoting, Compression, and File Format
compression: CompressionOptions = "infer",
thousands: str | None = None,
decimal: str = ".",
lineterminator: str | None = None,
quotechar: str = '"',
quoting: int = csv.QUOTE_MINIMAL,
doublequote: bool = True,
escapechar: str | None = None,
comment: str | None = None,
encoding: str | None = None,
encoding_errors: str | None = "strict",
dialect: str | csv.Dialect | None = None,
# Error Handling
on_bad_lines: str = "error",
# Internal
delim_whitespace: bool | lib.NoDefault = lib.no_default,
low_memory: bool = _c_parser_defaults["low_memory"],
memory_map: bool = False,
float_precision: Literal["high", "legacy"] | None = None,
storage_options: StorageOptions | None = None,
dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
) -> DataFrame | TextFileReader:
if keep_date_col is not lib.no_default:
# GH#55569
warnings.warn(
"The 'keep_date_col' keyword in pd.read_csv is deprecated and "
"will be removed in a future version. Explicitly remove unwanted "
"columns after parsing instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
else:
keep_date_col = False
if lib.is_list_like(parse_dates):
# GH#55569
depr = False
# error: Item "bool" of "bool | Sequence[Hashable] | None" has no
# attribute "__iter__" (not iterable)
if not all(is_hashable(x) for x in parse_dates): # type: ignore[union-attr]
depr = True
elif isinstance(parse_dates, dict) and any(
lib.is_list_like(x) for x in parse_dates.values()
):
depr = True
if depr:
warnings.warn(
"Support for nested sequences for 'parse_dates' in pd.read_csv "
"is deprecated. Combine the desired columns with pd.to_datetime "
"after parsing instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
if infer_datetime_format is not lib.no_default:
warnings.warn(
"The argument 'infer_datetime_format' is deprecated and will "
"be removed in a future version. "
"A strict version of it is now the default, see "
"https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. "
"You can safely remove this argument.",
FutureWarning,
stacklevel=find_stack_level(),
)
if delim_whitespace is not lib.no_default:
# GH#55569
warnings.warn(
"The 'delim_whitespace' keyword in pd.read_csv is deprecated and "
"will be removed in a future version. Use ``sep='\\s+'`` instead",
FutureWarning,
stacklevel=find_stack_level(),
)
else:
delim_whitespace = False
if verbose is not lib.no_default:
# GH#55569
warnings.warn(
"The 'verbose' keyword in pd.read_csv is deprecated and "
"will be removed in a future version.",
FutureWarning,
stacklevel=find_stack_level(),
)
else:
verbose = False
# locals() should never be modified
kwds = locals().copy()
del kwds["filepath_or_buffer"]
del kwds["sep"]
kwds_defaults = _refine_defaults_read(
dialect,
delimiter,
delim_whitespace,
engine,
sep,
on_bad_lines,
names,
defaults={"delimiter": ","},
dtype_backend=dtype_backend,
)
kwds.update(kwds_defaults)
return _read(filepath_or_buffer, kwds)
# iterator=True -> TextFileReader
@overload
def read_table(
filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
*,
sep: str | None | lib.NoDefault = ...,
delimiter: str | None | lib.NoDefault = ...,
header: int | Sequence[int] | None | Literal["infer"] = ...,
names: Sequence[Hashable] | None | lib.NoDefault = ...,
index_col: IndexLabel | Literal[False] | None = ...,
usecols: UsecolsArgType = ...,
dtype: DtypeArg | None = ...,
engine: CSVEngine | None = ...,
converters: Mapping[Hashable, Callable] | None = ...,
true_values: list | None = ...,
false_values: list | None = ...,
skipinitialspace: bool = ...,
skiprows: list[int] | int | Callable[[Hashable], bool] | None = ...,
skipfooter: int = ...,
nrows: int | None = ...,
na_values: Sequence[str] | Mapping[str, Sequence[str]] | None = ...,
keep_default_na: bool = ...,
na_filter: bool = ...,
verbose: bool | lib.NoDefault = ...,
skip_blank_lines: bool = ...,
parse_dates: bool | Sequence[Hashable] = ...,
infer_datetime_format: bool | lib.NoDefault = ...,
keep_date_col: bool | lib.NoDefault = ...,
date_parser: Callable | lib.NoDefault = ...,
date_format: str | dict[Hashable, str] | None = ...,
dayfirst: bool = ...,
cache_dates: bool = ...,
iterator: Literal[True],
chunksize: int | None = ...,
compression: CompressionOptions = ...,
thousands: str | None = ...,
decimal: str = ...,
lineterminator: str | None = ...,
quotechar: str = ...,
quoting: int = ...,
doublequote: bool = ...,
escapechar: str | None = ...,
comment: str | None = ...,
encoding: str | None = ...,
encoding_errors: str | None = ...,
dialect: str | csv.Dialect | None = ...,
on_bad_lines=...,
delim_whitespace: bool = ...,
low_memory: bool = ...,
memory_map: bool = ...,
float_precision: str | None = ...,
storage_options: StorageOptions = ...,
dtype_backend: DtypeBackend | lib.NoDefault = ...,
) -> TextFileReader:
...
# chunksize=int -> TextFileReader
@overload
def read_table(
filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
*,
sep: str | None | lib.NoDefault = ...,
delimiter: str | None | lib.NoDefault = ...,
header: int | Sequence[int] | None | Literal["infer"] = ...,
names: Sequence[Hashable] | None | lib.NoDefault = ...,
index_col: IndexLabel | Literal[False] | None = ...,
usecols: UsecolsArgType = ...,
dtype: DtypeArg | None = ...,
engine: CSVEngine | None = ...,
converters: Mapping[Hashable, Callable] | None = ...,
true_values: list | None = ...,
false_values: list | None = ...,
skipinitialspace: bool = ...,
skiprows: list[int] | int | Callable[[Hashable], bool] | None = ...,
skipfooter: int = ...,
nrows: int | None = ...,
na_values: Sequence[str] | Mapping[str, Sequence[str]] | None = ...,
keep_default_na: bool = ...,
na_filter: bool = ...,
verbose: bool | lib.NoDefault = ...,
skip_blank_lines: bool = ...,
parse_dates: bool | Sequence[Hashable] = ...,
infer_datetime_format: bool | lib.NoDefault = ...,
keep_date_col: bool | lib.NoDefault = ...,
date_parser: Callable | lib.NoDefault = ...,
date_format: str | dict[Hashable, str] | None = ...,
dayfirst: bool = ...,
cache_dates: bool = ...,
iterator: bool = ...,
chunksize: int,
compression: CompressionOptions = ...,
thousands: str | None = ...,
decimal: str = ...,
lineterminator: str | None = ...,
quotechar: str = ...,
quoting: int = ...,
doublequote: bool = ...,
escapechar: str | None = ...,
comment: str | None = ...,
encoding: str | None = ...,
encoding_errors: str | None = ...,
dialect: str | csv.Dialect | None = ...,
on_bad_lines=...,
delim_whitespace: bool = ...,
low_memory: bool = ...,
memory_map: bool = ...,
float_precision: str | None = ...,
storage_options: StorageOptions = ...,
dtype_backend: DtypeBackend | lib.NoDefault = ...,
) -> TextFileReader:
...
# default -> DataFrame
@overload
def read_table(
filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
*,
sep: str | None | lib.NoDefault = ...,
delimiter: str | None | lib.NoDefault = ...,
header: int | Sequence[int] | None | Literal["infer"] = ...,
names: Sequence[Hashable] | None | lib.NoDefault = ...,
index_col: IndexLabel | Literal[False] | None = ...,
usecols: UsecolsArgType = ...,
dtype: DtypeArg | None = ...,
engine: CSVEngine | None = ...,
converters: Mapping[Hashable, Callable] | None = ...,
true_values: list | None = ...,
false_values: list | None = ...,
skipinitialspace: bool = ...,
skiprows: list[int] | int | Callable[[Hashable], bool] | None = ...,
skipfooter: int = ...,
nrows: int | None = ...,
na_values: Sequence[str] | Mapping[str, Sequence[str]] | None = ...,
keep_default_na: bool = ...,
na_filter: bool = ...,
verbose: bool | lib.NoDefault = ...,
skip_blank_lines: bool = ...,
parse_dates: bool | Sequence[Hashable] = ...,
infer_datetime_format: bool | lib.NoDefault = ...,
keep_date_col: bool | lib.NoDefault = ...,
date_parser: Callable | lib.NoDefault = ...,
date_format: str | dict[Hashable, str] | None = ...,
dayfirst: bool = ...,
cache_dates: bool = ...,
iterator: Literal[False] = ...,
chunksize: None = ...,
compression: CompressionOptions = ...,
thousands: str | None = ...,
decimal: str = ...,
lineterminator: str | None = ...,
quotechar: str = ...,
quoting: int = ...,
doublequote: bool = ...,
escapechar: str | None = ...,
comment: str | None = ...,
encoding: str | None = ...,
encoding_errors: str | None = ...,
dialect: str | csv.Dialect | None = ...,
on_bad_lines=...,
delim_whitespace: bool = ...,
low_memory: bool = ...,
memory_map: bool = ...,
float_precision: str | None = ...,
storage_options: StorageOptions = ...,
dtype_backend: DtypeBackend | lib.NoDefault = ...,
) -> DataFrame:
...
# Unions -> DataFrame | TextFileReader
@overload
def read_table(
filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
*,
sep: str | None | lib.NoDefault = ...,
delimiter: str | None | lib.NoDefault = ...,
header: int | Sequence[int] | None | Literal["infer"] = ...,
names: Sequence[Hashable] | None | lib.NoDefault = ...,
index_col: IndexLabel | Literal[False] | None = ...,
usecols: UsecolsArgType = ...,
dtype: DtypeArg | None = ...,
engine: CSVEngine | None = ...,
converters: Mapping[Hashable, Callable] | None = ...,
true_values: list | None = ...,
false_values: list | None = ...,
skipinitialspace: bool = ...,
skiprows: list[int] | int | Callable[[Hashable], bool] | None = ...,
skipfooter: int = ...,
nrows: int | None = ...,
na_values: Sequence[str] | Mapping[str, Sequence[str]] | None = ...,
keep_default_na: bool = ...,
na_filter: bool = ...,
verbose: bool | lib.NoDefault = ...,
skip_blank_lines: bool = ...,
parse_dates: bool | Sequence[Hashable] = ...,
infer_datetime_format: bool | lib.NoDefault = ...,
keep_date_col: bool | lib.NoDefault = ...,
date_parser: Callable | lib.NoDefault = ...,
date_format: str | dict[Hashable, str] | None = ...,
dayfirst: bool = ...,
cache_dates: bool = ...,
iterator: bool = ...,
chunksize: int | None = ...,
compression: CompressionOptions = ...,
thousands: str | None = ...,
decimal: str = ...,
lineterminator: str | None = ...,
quotechar: str = ...,
quoting: int = ...,
doublequote: bool = ...,
escapechar: str | None = ...,
comment: str | None = ...,
encoding: str | None = ...,
encoding_errors: str | None = ...,
dialect: str | csv.Dialect | None = ...,
on_bad_lines=...,
delim_whitespace: bool = ...,
low_memory: bool = ...,
memory_map: bool = ...,
float_precision: str | None = ...,
storage_options: StorageOptions = ...,
dtype_backend: DtypeBackend | lib.NoDefault = ...,
) -> DataFrame | TextFileReader:
...
@Appender(
_doc_read_csv_and_table.format(
func_name="read_table",
summary="Read general delimited file into DataFrame.",
see_also_func_name="read_csv",
see_also_func_summary=(
"Read a comma-separated values (csv) file into DataFrame."
),
_default_sep=r"'\\t' (tab-stop)",
storage_options=_shared_docs["storage_options"],
decompression_options=_shared_docs["decompression_options"]
% "filepath_or_buffer",
)
)
def read_table(
filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
*,
sep: str | None | lib.NoDefault = lib.no_default,
delimiter: str | None | lib.NoDefault = None,
# Column and Index Locations and Names
header: int | Sequence[int] | None | Literal["infer"] = "infer",
names: Sequence[Hashable] | None | lib.NoDefault = lib.no_default,
index_col: IndexLabel | Literal[False] | None = None,
usecols: UsecolsArgType = None,
# General Parsing Configuration
dtype: DtypeArg | None = None,
engine: CSVEngine | None = None,
converters: Mapping[Hashable, Callable] | None = None,
true_values: list | None = None,
false_values: list | None = None,
skipinitialspace: bool = False,
skiprows: list[int] | int | Callable[[Hashable], bool] | None = None,
skipfooter: int = 0,
nrows: int | None = None,
# NA and Missing Data Handling
na_values: Sequence[str] | Mapping[str, Sequence[str]] | None = None,
keep_default_na: bool = True,
na_filter: bool = True,
verbose: bool | lib.NoDefault = lib.no_default,
skip_blank_lines: bool = True,
# Datetime Handling
parse_dates: bool | Sequence[Hashable] = False,
infer_datetime_format: bool | lib.NoDefault = lib.no_default,
keep_date_col: bool | lib.NoDefault = lib.no_default,
date_parser: Callable | lib.NoDefault = lib.no_default,
date_format: str | dict[Hashable, str] | None = None,
dayfirst: bool = False,
cache_dates: bool = True,
# Iteration
iterator: bool = False,
chunksize: int | None = None,
# Quoting, Compression, and File Format
compression: CompressionOptions = "infer",
thousands: str | None = None,
decimal: str = ".",
lineterminator: str | None = None,
quotechar: str = '"',
quoting: int = csv.QUOTE_MINIMAL,
doublequote: bool = True,
escapechar: str | None = None,
comment: str | None = None,
encoding: str | None = None,
encoding_errors: str | None = "strict",
dialect: str | csv.Dialect | None = None,
# Error Handling
on_bad_lines: str = "error",
# Internal
delim_whitespace: bool | lib.NoDefault = lib.no_default,
low_memory: bool = _c_parser_defaults["low_memory"],
memory_map: bool = False,
float_precision: str | None = None,
storage_options: StorageOptions | None = None,
dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
) -> DataFrame | TextFileReader:
if keep_date_col is not lib.no_default:
# GH#55569
warnings.warn(
"The 'keep_date_col' keyword in pd.read_table is deprecated and "
"will be removed in a future version. Explicitly remove unwanted "
"columns after parsing instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
else:
keep_date_col = False
# error: Item "bool" of "bool | Sequence[Hashable]" has no attribute "__iter__"
if lib.is_list_like(parse_dates) and not all(is_hashable(x) for x in parse_dates): # type: ignore[union-attr]
# GH#55569
warnings.warn(
"Support for nested sequences for 'parse_dates' in pd.read_table "
"is deprecated. Combine the desired columns with pd.to_datetime "
"after parsing instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
if infer_datetime_format is not lib.no_default:
warnings.warn(
"The argument 'infer_datetime_format' is deprecated and will "
"be removed in a future version. "
"A strict version of it is now the default, see "
"https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. "
"You can safely remove this argument.",
FutureWarning,
stacklevel=find_stack_level(),
)
if delim_whitespace is not lib.no_default:
# GH#55569
warnings.warn(
"The 'delim_whitespace' keyword in pd.read_table is deprecated and "
"will be removed in a future version. Use ``sep='\\s+'`` instead",
FutureWarning,
stacklevel=find_stack_level(),
)
else:
delim_whitespace = False
if verbose is not lib.no_default:
# GH#55569
warnings.warn(
"The 'verbose' keyword in pd.read_table is deprecated and "
"will be removed in a future version.",
FutureWarning,
stacklevel=find_stack_level(),
)
else:
verbose = False
# locals() should never be modified
kwds = locals().copy()
del kwds["filepath_or_buffer"]
del kwds["sep"]
kwds_defaults = _refine_defaults_read(
dialect,
delimiter,
delim_whitespace,
engine,
sep,
on_bad_lines,
names,
defaults={"delimiter": "\t"},
dtype_backend=dtype_backend,
)
kwds.update(kwds_defaults)
return _read(filepath_or_buffer, kwds)
@overload
def read_fwf(
filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
*,
colspecs: Sequence[tuple[int, int]] | str | None = ...,
widths: Sequence[int] | None = ...,
infer_nrows: int = ...,
dtype_backend: DtypeBackend | lib.NoDefault = ...,
iterator: Literal[True],
chunksize: int | None = ...,
**kwds,
) -> TextFileReader:
...
@overload
def read_fwf(
filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
*,
colspecs: Sequence[tuple[int, int]] | str | None = ...,
widths: Sequence[int] | None = ...,
infer_nrows: int = ...,
dtype_backend: DtypeBackend | lib.NoDefault = ...,
iterator: bool = ...,
chunksize: int,
**kwds,
) -> TextFileReader:
...
@overload
def read_fwf(
filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
*,
colspecs: Sequence[tuple[int, int]] | str | None = ...,
widths: Sequence[int] | None = ...,
infer_nrows: int = ...,
dtype_backend: DtypeBackend | lib.NoDefault = ...,
iterator: Literal[False] = ...,
chunksize: None = ...,
**kwds,
) -> DataFrame:
...
def read_fwf(
filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
*,
colspecs: Sequence[tuple[int, int]] | str | None = "infer",
widths: Sequence[int] | None = None,
infer_nrows: int = 100,
dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
iterator: bool = False,
chunksize: int | None = None,
**kwds,
) -> DataFrame | TextFileReader:
r"""
Read a table of fixed-width formatted lines into DataFrame.
Also supports optionally iterating or breaking of the file
into chunks.
Additional help can be found in the `online docs for IO Tools
<https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_.
Parameters
----------
filepath_or_buffer : str, path object, or file-like object
String, path object (implementing ``os.PathLike[str]``), or file-like
object implementing a text ``read()`` function.The string could be a URL.
Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is
expected. A local file could be:
``file://localhost/path/to/table.csv``.
colspecs : list of tuple (int, int) or 'infer'. optional
A list of tuples giving the extents of the fixed-width
fields of each line as half-open intervals (i.e., [from, to[ ).
String value 'infer' can be used to instruct the parser to try
detecting the column specifications from the first 100 rows of
the data which are not being skipped via skiprows (default='infer').
widths : list of int, optional
A list of field widths which can be used instead of 'colspecs' if
the intervals are contiguous.
infer_nrows : int, default 100
The number of rows to consider when letting the parser determine the
`colspecs`.
dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
Back-end data type applied to the resultant :class:`DataFrame`
(still experimental). Behaviour is as follows:
* ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
(default).
* ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
DataFrame.
.. versionadded:: 2.0
**kwds : optional
Optional keyword arguments can be passed to ``TextFileReader``.
Returns
-------
DataFrame or TextFileReader
A comma-separated values (csv) file is returned as two-dimensional
data structure with labeled axes.
See Also
--------
DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
read_csv : Read a comma-separated values (csv) file into DataFrame.
Examples
--------
>>> pd.read_fwf('data.csv') # doctest: +SKIP
"""
# Check input arguments.
if colspecs is None and widths is None:
raise ValueError("Must specify either colspecs or widths")
if colspecs not in (None, "infer") and widths is not None:
raise ValueError("You must specify only one of 'widths' and 'colspecs'")
# Compute 'colspecs' from 'widths', if specified.
if widths is not None:
colspecs, col = [], 0
for w in widths:
colspecs.append((col, col + w))
col += w
# for mypy
assert colspecs is not None
# GH#40830
# Ensure length of `colspecs` matches length of `names`
names = kwds.get("names")
if names is not None:
if len(names) != len(colspecs) and colspecs != "infer":
# need to check len(index_col) as it might contain
# unnamed indices, in which case it's name is not required
len_index = 0
if kwds.get("index_col") is not None:
index_col: Any = kwds.get("index_col")
if index_col is not False:
if not is_list_like(index_col):
len_index = 1
else:
len_index = len(index_col)
if kwds.get("usecols") is None and len(names) + len_index != len(colspecs):
# If usecols is used colspec may be longer than names
raise ValueError("Length of colspecs must match length of names")
kwds["colspecs"] = colspecs
kwds["infer_nrows"] = infer_nrows
kwds["engine"] = "python-fwf"
kwds["iterator"] = iterator
kwds["chunksize"] = chunksize
check_dtype_backend(dtype_backend)
kwds["dtype_backend"] = dtype_backend
return _read(filepath_or_buffer, kwds)
class TextFileReader(abc.Iterator):
"""
Passed dialect overrides any of the related parser options
"""
def __init__(
self,
f: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str] | list,
engine: CSVEngine | None = None,
**kwds,
) -> None:
if engine is not None:
engine_specified = True
else:
engine = "python"
engine_specified = False
self.engine = engine
self._engine_specified = kwds.get("engine_specified", engine_specified)
_validate_skipfooter(kwds)
dialect = _extract_dialect(kwds)
if dialect is not None:
if engine == "pyarrow":
raise ValueError(
"The 'dialect' option is not supported with the 'pyarrow' engine"
)
kwds = _merge_with_dialect_properties(dialect, kwds)
if kwds.get("header", "infer") == "infer":
kwds["header"] = 0 if kwds.get("names") is None else None
self.orig_options = kwds
# miscellanea
self._currow = 0
options = self._get_options_with_defaults(engine)
options["storage_options"] = kwds.get("storage_options", None)
self.chunksize = options.pop("chunksize", None)
self.nrows = options.pop("nrows", None)
self._check_file_or_buffer(f, engine)
self.options, self.engine = self._clean_options(options, engine)
if "has_index_names" in kwds:
self.options["has_index_names"] = kwds["has_index_names"]
self.handles: IOHandles | None = None
self._engine = self._make_engine(f, self.engine)
def close(self) -> None:
if self.handles is not None:
self.handles.close()
self._engine.close()
def _get_options_with_defaults(self, engine: CSVEngine) -> dict[str, Any]:
kwds = self.orig_options
options = {}
default: object | None
for argname, default in parser_defaults.items():
value = kwds.get(argname, default)
# see gh-12935
if (
engine == "pyarrow"
and argname in _pyarrow_unsupported
and value != default
and value != getattr(value, "value", default)
):
raise ValueError(
f"The {repr(argname)} option is not supported with the "
f"'pyarrow' engine"
)
options[argname] = value
for argname, default in _c_parser_defaults.items():
if argname in kwds:
value = kwds[argname]
if engine != "c" and value != default:
# TODO: Refactor this logic, its pretty convoluted
if "python" in engine and argname not in _python_unsupported:
pass
elif "pyarrow" in engine and argname not in _pyarrow_unsupported:
pass
else:
raise ValueError(
f"The {repr(argname)} option is not supported with the "
f"{repr(engine)} engine"
)
else:
value = default
options[argname] = value
if engine == "python-fwf":
for argname, default in _fwf_defaults.items():
options[argname] = kwds.get(argname, default)
return options
def _check_file_or_buffer(self, f, engine: CSVEngine) -> None:
# see gh-16530
if is_file_like(f) and engine != "c" and not hasattr(f, "__iter__"):
# The C engine doesn't need the file-like to have the "__iter__"
# attribute. However, the Python engine needs "__iter__(...)"
# when iterating through such an object, meaning it
# needs to have that attribute
raise ValueError(
"The 'python' engine cannot iterate through this file buffer."
)
def _clean_options(
self, options: dict[str, Any], engine: CSVEngine
) -> tuple[dict[str, Any], CSVEngine]:
result = options.copy()
fallback_reason = None
# C engine not supported yet
if engine == "c":
if options["skipfooter"] > 0:
fallback_reason = "the 'c' engine does not support skipfooter"
engine = "python"
sep = options["delimiter"]
delim_whitespace = options["delim_whitespace"]
if sep is None and not delim_whitespace:
if engine in ("c", "pyarrow"):
fallback_reason = (
f"the '{engine}' engine does not support "
"sep=None with delim_whitespace=False"
)
engine = "python"
elif sep is not None and len(sep) > 1:
if engine == "c" and sep == r"\s+":
result["delim_whitespace"] = True
del result["delimiter"]
elif engine not in ("python", "python-fwf"):
# wait until regex engine integrated
fallback_reason = (
f"the '{engine}' engine does not support "
"regex separators (separators > 1 char and "
r"different from '\s+' are interpreted as regex)"
)
engine = "python"
elif delim_whitespace:
if "python" in engine:
result["delimiter"] = r"\s+"
elif sep is not None:
encodeable = True
encoding = sys.getfilesystemencoding() or "utf-8"
try:
if len(sep.encode(encoding)) > 1:
encodeable = False
except UnicodeDecodeError:
encodeable = False
if not encodeable and engine not in ("python", "python-fwf"):
fallback_reason = (
f"the separator encoded in {encoding} "
f"is > 1 char long, and the '{engine}' engine "
"does not support such separators"
)
engine = "python"
quotechar = options["quotechar"]
if quotechar is not None and isinstance(quotechar, (str, bytes)):
if (
len(quotechar) == 1
and ord(quotechar) > 127
and engine not in ("python", "python-fwf")
):
fallback_reason = (
"ord(quotechar) > 127, meaning the "
"quotechar is larger than one byte, "
f"and the '{engine}' engine does not support such quotechars"
)
engine = "python"
if fallback_reason and self._engine_specified:
raise ValueError(fallback_reason)
if engine == "c":
for arg in _c_unsupported:
del result[arg]
if "python" in engine:
for arg in _python_unsupported:
if fallback_reason and result[arg] != _c_parser_defaults.get(arg):
raise ValueError(
"Falling back to the 'python' engine because "
f"{fallback_reason}, but this causes {repr(arg)} to be "
"ignored as it is not supported by the 'python' engine."
)
del result[arg]
if fallback_reason:
warnings.warn(
(
"Falling back to the 'python' engine because "
f"{fallback_reason}; you can avoid this warning by specifying "
"engine='python'."
),
ParserWarning,
stacklevel=find_stack_level(),
)
index_col = options["index_col"]
names = options["names"]
converters = options["converters"]
na_values = options["na_values"]
skiprows = options["skiprows"]
validate_header_arg(options["header"])
if index_col is True:
raise ValueError("The value of index_col couldn't be 'True'")
if is_index_col(index_col):
if not isinstance(index_col, (list, tuple, np.ndarray)):
index_col = [index_col]
result["index_col"] = index_col
names = list(names) if names is not None else names
# type conversion-related
if converters is not None:
if not isinstance(converters, dict):
raise TypeError(
"Type converters must be a dict or subclass, "
f"input was a {type(converters).__name__}"
)
else:
converters = {}
# Converting values to NA
keep_default_na = options["keep_default_na"]
floatify = engine != "pyarrow"
na_values, na_fvalues = _clean_na_values(
na_values, keep_default_na, floatify=floatify
)
# handle skiprows; this is internally handled by the
# c-engine, so only need for python and pyarrow parsers
if engine == "pyarrow":
if not is_integer(skiprows) and skiprows is not None:
# pyarrow expects skiprows to be passed as an integer
raise ValueError(
"skiprows argument must be an integer when using "
"engine='pyarrow'"
)
else:
if is_integer(skiprows):
skiprows = list(range(skiprows))
if skiprows is None:
skiprows = set()
elif not callable(skiprows):
skiprows = set(skiprows)
# put stuff back
result["names"] = names
result["converters"] = converters
result["na_values"] = na_values
result["na_fvalues"] = na_fvalues
result["skiprows"] = skiprows
return result, engine
def __next__(self) -> DataFrame:
try:
return self.get_chunk()
except StopIteration:
self.close()
raise
def _make_engine(
self,
f: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str] | list | IO,
engine: CSVEngine = "c",
) -> ParserBase:
mapping: dict[str, type[ParserBase]] = {
"c": CParserWrapper,
"python": PythonParser,
"pyarrow": ArrowParserWrapper,
"python-fwf": FixedWidthFieldParser,
}
if engine not in mapping:
raise ValueError(
f"Unknown engine: {engine} (valid options are {mapping.keys()})"
)
if not isinstance(f, list):
# open file here
is_text = True
mode = "r"
if engine == "pyarrow":
is_text = False
mode = "rb"
elif (
engine == "c"
and self.options.get("encoding", "utf-8") == "utf-8"
and isinstance(stringify_path(f), str)
):
# c engine can decode utf-8 bytes, adding TextIOWrapper makes
# the c-engine especially for memory_map=True far slower
is_text = False
if "b" not in mode:
mode += "b"
self.handles = get_handle(
f,
mode,
encoding=self.options.get("encoding", None),
compression=self.options.get("compression", None),
memory_map=self.options.get("memory_map", False),
is_text=is_text,
errors=self.options.get("encoding_errors", "strict"),
storage_options=self.options.get("storage_options", None),
)
assert self.handles is not None
f = self.handles.handle
elif engine != "python":
msg = f"Invalid file path or buffer object type: {type(f)}"
raise ValueError(msg)
try:
return mapping[engine](f, **self.options)
except Exception:
if self.handles is not None:
self.handles.close()
raise
def _failover_to_python(self) -> None:
raise AbstractMethodError(self)
def read(self, nrows: int | None = None) -> DataFrame:
if self.engine == "pyarrow":
try:
# error: "ParserBase" has no attribute "read"
df = self._engine.read() # type: ignore[attr-defined]
except Exception:
self.close()
raise
else:
nrows = validate_integer("nrows", nrows)
try:
# error: "ParserBase" has no attribute "read"
(
index,
columns,
col_dict,
) = self._engine.read( # type: ignore[attr-defined]
nrows
)
except Exception:
self.close()
raise
if index is None:
if col_dict:
# Any column is actually fine:
new_rows = len(next(iter(col_dict.values())))
index = RangeIndex(self._currow, self._currow + new_rows)
else:
new_rows = 0
else:
new_rows = len(index)
if hasattr(self, "orig_options"):
dtype_arg = self.orig_options.get("dtype", None)
else:
dtype_arg = None
if isinstance(dtype_arg, dict):
dtype = defaultdict(lambda: None) # type: ignore[var-annotated]
dtype.update(dtype_arg)
elif dtype_arg is not None and pandas_dtype(dtype_arg) in (
np.str_,
np.object_,
):
dtype = defaultdict(lambda: dtype_arg)
else:
dtype = None
if dtype is not None:
new_col_dict = {}
for k, v in col_dict.items():
d = (
dtype[k]
if pandas_dtype(dtype[k]) in (np.str_, np.object_)
else None
)
new_col_dict[k] = Series(v, index=index, dtype=d, copy=False)
else:
new_col_dict = col_dict
df = DataFrame(
new_col_dict,
columns=columns,
index=index,
copy=not using_copy_on_write(),
)
self._currow += new_rows
return df
def get_chunk(self, size: int | None = None) -> DataFrame:
if size is None:
size = self.chunksize
if self.nrows is not None:
if self._currow >= self.nrows:
raise StopIteration
size = min(size, self.nrows - self._currow)
return self.read(nrows=size)
def __enter__(self) -> Self:
return self
def __exit__(
self,
exc_type: type[BaseException] | None,
exc_value: BaseException | None,
traceback: TracebackType | None,
) -> None:
self.close()
def TextParser(*args, **kwds) -> TextFileReader:
"""
Converts lists of lists/tuples into DataFrames with proper type inference
and optional (e.g. string to datetime) conversion. Also enables iterating
lazily over chunks of large files
Parameters
----------
data : file-like object or list
delimiter : separator character to use
dialect : str or csv.Dialect instance, optional
Ignored if delimiter is longer than 1 character
names : sequence, default
header : int, default 0
Row to use to parse column labels. Defaults to the first row. Prior
rows will be discarded
index_col : int or list, optional
Column or columns to use as the (possibly hierarchical) index
has_index_names: bool, default False
True if the cols defined in index_col have an index name and are
not in the header.
na_values : scalar, str, list-like, or dict, optional
Additional strings to recognize as NA/NaN.
keep_default_na : bool, default True
thousands : str, optional
Thousands separator
comment : str, optional
Comment out remainder of line
parse_dates : bool, default False
keep_date_col : bool, default False
date_parser : function, optional
.. deprecated:: 2.0.0
date_format : str or dict of column -> format, default ``None``
.. versionadded:: 2.0.0
skiprows : list of integers
Row numbers to skip
skipfooter : int
Number of line at bottom of file to skip
converters : dict, optional
Dict of functions for converting values in certain columns. Keys can
either be integers or column labels, values are functions that take one
input argument, the cell (not column) content, and return the
transformed content.
encoding : str, optional
Encoding to use for UTF when reading/writing (ex. 'utf-8')
float_precision : str, optional
Specifies which converter the C engine should use for floating-point
values. The options are `None` or `high` for the ordinary converter,
`legacy` for the original lower precision pandas converter, and
`round_trip` for the round-trip converter.
"""
kwds["engine"] = "python"
return TextFileReader(*args, **kwds)
def _clean_na_values(na_values, keep_default_na: bool = True, floatify: bool = True):
na_fvalues: set | dict
if na_values is None:
if keep_default_na:
na_values = STR_NA_VALUES
else:
na_values = set()
na_fvalues = set()
elif isinstance(na_values, dict):
old_na_values = na_values.copy()
na_values = {} # Prevent aliasing.
# Convert the values in the na_values dictionary
# into array-likes for further use. This is also
# where we append the default NaN values, provided
# that `keep_default_na=True`.
for k, v in old_na_values.items():
if not is_list_like(v):
v = [v]
if keep_default_na:
v = set(v) | STR_NA_VALUES
na_values[k] = v
na_fvalues = {k: _floatify_na_values(v) for k, v in na_values.items()}
else:
if not is_list_like(na_values):
na_values = [na_values]
na_values = _stringify_na_values(na_values, floatify)
if keep_default_na:
na_values = na_values | STR_NA_VALUES
na_fvalues = _floatify_na_values(na_values)
return na_values, na_fvalues
def _floatify_na_values(na_values):
# create float versions of the na_values
result = set()
for v in na_values:
try:
v = float(v)
if not np.isnan(v):
result.add(v)
except (TypeError, ValueError, OverflowError):
pass
return result
def _stringify_na_values(na_values, floatify: bool):
"""return a stringified and numeric for these values"""
result: list[str | float] = []
for x in na_values:
result.append(str(x))
result.append(x)
try:
v = float(x)
# we are like 999 here
if v == int(v):
v = int(v)
result.append(f"{v}.0")
result.append(str(v))
if floatify:
result.append(v)
except (TypeError, ValueError, OverflowError):
pass
if floatify:
try:
result.append(int(x))
except (TypeError, ValueError, OverflowError):
pass
return set(result)
def _refine_defaults_read(
dialect: str | csv.Dialect | None,
delimiter: str | None | lib.NoDefault,
delim_whitespace: bool,
engine: CSVEngine | None,
sep: str | None | lib.NoDefault,
on_bad_lines: str | Callable,
names: Sequence[Hashable] | None | lib.NoDefault,
defaults: dict[str, Any],
dtype_backend: DtypeBackend | lib.NoDefault,
):
"""Validate/refine default values of input parameters of read_csv, read_table.
Parameters
----------
dialect : str or csv.Dialect
If provided, this parameter will override values (default or not) for the
following parameters: `delimiter`, `doublequote`, `escapechar`,
`skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to
override values, a ParserWarning will be issued. See csv.Dialect
documentation for more details.
delimiter : str or object
Alias for sep.
delim_whitespace : bool
Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be
used as the sep. Equivalent to setting ``sep='\\s+'``. If this option
is set to True, nothing should be passed in for the ``delimiter``
parameter.
.. deprecated:: 2.2.0
Use ``sep="\\s+"`` instead.
engine : {{'c', 'python'}}
Parser engine to use. The C engine is faster while the python engine is
currently more feature-complete.
sep : str or object
A delimiter provided by the user (str) or a sentinel value, i.e.
pandas._libs.lib.no_default.
on_bad_lines : str, callable
An option for handling bad lines or a sentinel value(None).
names : array-like, optional
List of column names to use. If the file contains a header row,
then you should explicitly pass ``header=0`` to override the column names.
Duplicates in this list are not allowed.
defaults: dict
Default values of input parameters.
Returns
-------
kwds : dict
Input parameters with correct values.
Raises
------
ValueError :
If a delimiter was specified with ``sep`` (or ``delimiter``) and
``delim_whitespace=True``.
"""
# fix types for sep, delimiter to Union(str, Any)
delim_default = defaults["delimiter"]
kwds: dict[str, Any] = {}
# gh-23761
#
# When a dialect is passed, it overrides any of the overlapping
# parameters passed in directly. We don't want to warn if the
# default parameters were passed in (since it probably means
# that the user didn't pass them in explicitly in the first place).
#
# "delimiter" is the annoying corner case because we alias it to
# "sep" before doing comparison to the dialect values later on.
# Thus, we need a flag to indicate that we need to "override"
# the comparison to dialect values by checking if default values
# for BOTH "delimiter" and "sep" were provided.
if dialect is not None:
kwds["sep_override"] = delimiter is None and (
sep is lib.no_default or sep == delim_default
)
if delimiter and (sep is not lib.no_default):
raise ValueError("Specified a sep and a delimiter; you can only specify one.")
kwds["names"] = None if names is lib.no_default else names
# Alias sep -> delimiter.
if delimiter is None:
delimiter = sep
if delim_whitespace and (delimiter is not lib.no_default):
raise ValueError(
"Specified a delimiter with both sep and "
"delim_whitespace=True; you can only specify one."
)
if delimiter == "\n":
raise ValueError(
r"Specified \n as separator or delimiter. This forces the python engine "
"which does not accept a line terminator. Hence it is not allowed to use "
"the line terminator as separator.",
)
if delimiter is lib.no_default:
# assign default separator value
kwds["delimiter"] = delim_default
else:
kwds["delimiter"] = delimiter
if engine is not None:
kwds["engine_specified"] = True
else:
kwds["engine"] = "c"
kwds["engine_specified"] = False
if on_bad_lines == "error":
kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.ERROR
elif on_bad_lines == "warn":
kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.WARN
elif on_bad_lines == "skip":
kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.SKIP
elif callable(on_bad_lines):
if engine not in ["python", "pyarrow"]:
raise ValueError(
"on_bad_line can only be a callable function "
"if engine='python' or 'pyarrow'"
)
kwds["on_bad_lines"] = on_bad_lines
else:
raise ValueError(f"Argument {on_bad_lines} is invalid for on_bad_lines")
check_dtype_backend(dtype_backend)
kwds["dtype_backend"] = dtype_backend
return kwds
def _extract_dialect(kwds: dict[str, Any]) -> csv.Dialect | None:
"""
Extract concrete csv dialect instance.
Returns
-------
csv.Dialect or None
"""
if kwds.get("dialect") is None:
return None
dialect = kwds["dialect"]
if dialect in csv.list_dialects():
dialect = csv.get_dialect(dialect)
_validate_dialect(dialect)
return dialect
MANDATORY_DIALECT_ATTRS = (
"delimiter",
"doublequote",
"escapechar",
"skipinitialspace",
"quotechar",
"quoting",
)
def _validate_dialect(dialect: csv.Dialect) -> None:
"""
Validate csv dialect instance.
Raises
------
ValueError
If incorrect dialect is provided.
"""
for param in MANDATORY_DIALECT_ATTRS:
if not hasattr(dialect, param):
raise ValueError(f"Invalid dialect {dialect} provided")
def _merge_with_dialect_properties(
dialect: csv.Dialect,
defaults: dict[str, Any],
) -> dict[str, Any]:
"""
Merge default kwargs in TextFileReader with dialect parameters.
Parameters
----------
dialect : csv.Dialect
Concrete csv dialect. See csv.Dialect documentation for more details.
defaults : dict
Keyword arguments passed to TextFileReader.
Returns
-------
kwds : dict
Updated keyword arguments, merged with dialect parameters.
"""
kwds = defaults.copy()
for param in MANDATORY_DIALECT_ATTRS:
dialect_val = getattr(dialect, param)
parser_default = parser_defaults[param]
provided = kwds.get(param, parser_default)
# Messages for conflicting values between the dialect
# instance and the actual parameters provided.
conflict_msgs = []
# Don't warn if the default parameter was passed in,
# even if it conflicts with the dialect (gh-23761).
if provided not in (parser_default, dialect_val):
msg = (
f"Conflicting values for '{param}': '{provided}' was "
f"provided, but the dialect specifies '{dialect_val}'. "
"Using the dialect-specified value."
)
# Annoying corner case for not warning about
# conflicts between dialect and delimiter parameter.
# Refer to the outer "_read_" function for more info.
if not (param == "delimiter" and kwds.pop("sep_override", False)):
conflict_msgs.append(msg)
if conflict_msgs:
warnings.warn(
"\n\n".join(conflict_msgs), ParserWarning, stacklevel=find_stack_level()
)
kwds[param] = dialect_val
return kwds
def _validate_skipfooter(kwds: dict[str, Any]) -> None:
"""
Check whether skipfooter is compatible with other kwargs in TextFileReader.
Parameters
----------
kwds : dict
Keyword arguments passed to TextFileReader.
Raises
------
ValueError
If skipfooter is not compatible with other parameters.
"""
if kwds.get("skipfooter"):
if kwds.get("iterator") or kwds.get("chunksize"):
raise ValueError("'skipfooter' not supported for iteration")
if kwds.get("nrows"):
raise ValueError("'skipfooter' not supported with 'nrows'")
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