""" 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 typing import ( IO, TYPE_CHECKING, Any, Generic, Literal, Self, TypedDict, Unpack, cast, overload, ) import warnings import numpy as np from pandas._libs import lib from pandas._libs.parsers import STR_NA_VALUES from pandas.errors import ( AbstractMethodError, ParserWarning, ) from pandas.util._decorators import ( set_module, ) 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_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.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 ( Callable, Hashable, Iterable, Mapping, Sequence, ) from types import TracebackType from pandas._typing import ( CompressionOptions, CSVEngine, DtypeArg, DtypeBackend, FilePath, HashableT, IndexLabel, ReadCsvBuffer, StorageOptions, UsecolsArgType, ) class _read_shared(TypedDict, Generic[HashableT], total=False): # annotations shared between read_csv/fwf/table's overloads # NOTE: Keep in sync with the annotations of the implementation 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[HashableT, 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 skip_blank_lines: bool parse_dates: bool | Sequence[Hashable] | None date_format: str | dict[Hashable, str] | None dayfirst: bool cache_dates: bool 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: str low_memory: bool memory_map: bool float_precision: Literal["high", "legacy", "round_trip"] | None storage_options: StorageOptions | None dtype_backend: DtypeBackend | lib.NoDefault else: _read_shared = dict class _C_Parser_Defaults(TypedDict): na_filter: Literal[True] low_memory: Literal[True] memory_map: Literal[False] float_precision: None _c_parser_defaults: _C_Parser_Defaults = { "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", "quoting", "lineterminator", "converters", "iterator", "dayfirst", "skipinitialspace", "low_memory", } @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_format and parse_dates=False, we should not parse the # dates GH#44366 if kwds.get("parse_dates", None) is None: if 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) # Check type of encoding_errors errors = kwds.get("encoding_errors", "strict") if not isinstance(errors, str): raise ValueError( f"encoding_errors must be a string, got {type(errors).__name__}" ) 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) @overload def read_csv( filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str], *, iterator: Literal[True], chunksize: int | None = ..., **kwds: Unpack[_read_shared[HashableT]], ) -> TextFileReader: ... @overload def read_csv( filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str], *, iterator: bool = ..., chunksize: int, **kwds: Unpack[_read_shared[HashableT]], ) -> TextFileReader: ... @overload def read_csv( filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str], *, iterator: Literal[False] = ..., chunksize: None = ..., **kwds: Unpack[_read_shared[HashableT]], ) -> DataFrame: ... @overload def read_csv( filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str], *, iterator: bool = ..., chunksize: int | None = ..., **kwds: Unpack[_read_shared[HashableT]], ) -> DataFrame | TextFileReader: ... @set_module("pandas") 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[HashableT, 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, skip_blank_lines: bool = True, # Datetime Handling parse_dates: bool | Sequence[Hashable] | None = None, 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 low_memory: bool = _c_parser_defaults["low_memory"], memory_map: bool = False, float_precision: Literal["high", "legacy", "round_trip"] | None = None, storage_options: StorageOptions | None = None, dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default, ) -> DataFrame | TextFileReader: """ Read a comma-separated values (csv) file 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 `_. 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 ',' 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. When inferred from the file contents, headers are kept distinct from each other by renaming duplicate names with a numeric suffix of the form ``".{{count}}"`` starting from 1, e.g. ``"foo"`` and ``"foo.1"``. Empty headers are named ``"Unnamed: {{i}}"`` or `` "Unnamed: {{i}}_level_{{level}}"`` in the case of MultiIndex columns. 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. 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. Some features of the "pyarrow" engine are unsupported or may not work correctly. 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. Refers to the number of data rows in the returned DataFrame, excluding: * The header row containing column names. * Rows before the header row, if ``header=1`` or larger. Example usage: * To read the first 999,999 (non-header) rows: ``read_csv(..., nrows=999999)`` * To read rows 1,000,000 through 1,999,999: ``read_csv(..., skiprows=1000000, nrows=999999)`` 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``: empty string, "NaN", "N/A", "NULL", and other common representations of missing data. 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. skip_blank_lines : bool, default True If ``True``, skip over blank lines rather than interpreting as ``NaN`` values. parse_dates : bool, None, list of Hashable, default None The behavior is as follows: * ``bool``. If ``True`` -> try parsing the index. * ``None``. Behaves like ``True`` if ``date_format`` is specified. * ``list`` of ``int`` or names. e.g. If ``[1, 2, 3]`` -> try parsing columns 1, 2, 3 each as a separate date column. 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. date_format : str or dict of column -> format, optional Format to use for parsing dates and/or times when used in conjunction with ``parse_dates``. The strftime to parse time, e.g. :const:`"%d/%m/%Y"`. See `strftime documentation `_ 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 `_ 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 `_ for more information on ``iterator`` and ``chunksize``. compression : str or dict, default 'infer' For on-the-fly decompression of on-disk data. If 'infer' and 'filepath_or_buffer' is path-like, then detect compression from the following extensions: '.gz', '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2' (otherwise no compression). If using 'zip' or 'tar', the ZIP file must contain only one data file to be read in. Set to ``None`` for no decompression. Can also be a dict with key ``'method'`` set to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``, ``'tar'``} and other key-value pairs are forwarded to ``zipfile.ZipFile``, ``gzip.GzipFile``, ``bz2.BZ2File``, ``zstandard.ZstdDecompressor``, ``lzma.LZMAFile`` or ``tarfile.TarFile``, respectively. As an example, the following could be passed for Zstandard decompression using a custom compression dictionary: ``compression={'method': 'zstd', 'dict_data': my_compression_dict}``. 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 `_ . encoding_errors : str, optional, default 'strict' How encoding errors are treated. `List of possible values `_ . 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. - Callable, function that will process a single bad line. - With ``engine='python'``, function with signature ``(bad_line: list[str]) -> list[str] | None``. ``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. - With ``engine='pyarrow'``, function with signature as described in pyarrow documentation: `invalid_row_handler `_. .. versionchanged:: 2.2.0 Callable for ``engine='pyarrow'`` 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 : dict, optional Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to ``urllib.request.Request`` as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more details, and for more examples on storage options refer `here `_. dtype_backend : {{'numpy_nullable', 'pyarrow'}} Back-end data type applied to the resultant :class:`DataFrame` (still experimental). If not specified, the default behavior is to not use nullable data types. If specified, the behavior is as follows: * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame` * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype` :class:`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. read_table : Read general delimited file into DataFrame. read_fwf : Read a table of fixed-width formatted lines into DataFrame. Examples -------- >>> pd.read_csv("data.csv") # doctest: +SKIP Name Value 0 foo 1 1 bar 2 2 #baz 3 Index and header can be specified via the `index_col` and `header` arguments. >>> pd.read_csv("data.csv", header=None) # doctest: +SKIP 0 1 0 Name Value 1 foo 1 2 bar 2 3 #baz 3 >>> pd.read_csv("data.csv", index_col="Value") # doctest: +SKIP Name Value 1 foo 2 bar 3 #baz Column types are inferred but can be explicitly specified using the dtype argument. >>> pd.read_csv("data.csv", dtype={{"Value": float}}) # doctest: +SKIP Name Value 0 foo 1.0 1 bar 2.0 2 #baz 3.0 True, False, and NA values, and thousands separators have defaults, but can be explicitly specified, too. Supply the values you would like as strings or lists of strings! >>> pd.read_csv("data.csv", na_values=["foo", "bar"]) # doctest: +SKIP Name Value 0 NaN 1 1 NaN 2 2 #baz 3 Comment lines in the input file can be skipped using the `comment` argument. >>> pd.read_csv("data.csv", comment="#") # doctest: +SKIP Name Value 0 foo 1 1 bar 2 By default, columns with dates will be read as ``object`` rather than ``datetime``. >>> df = pd.read_csv("tmp.csv") # doctest: +SKIP >>> df # doctest: +SKIP col 1 col 2 col 3 0 10 10/04/2018 Sun 15 Jan 2023 1 20 15/04/2018 Fri 12 May 2023 >>> df.dtypes # doctest: +SKIP col 1 int64 col 2 object col 3 object dtype: object Specific columns can be parsed as dates by using the `parse_dates` and `date_format` arguments. >>> df = pd.read_csv( ... "tmp.csv", ... parse_dates=[1, 2], ... date_format={{"col 2": "%d/%m/%Y", "col 3": "%a %d %b %Y"}}, ... ) # doctest: +SKIP >>> df.dtypes # doctest: +SKIP col 1 int64 col 2 datetime64[ns] col 3 datetime64[ns] dtype: object """ # locals() should never be modified kwds = locals().copy() del kwds["filepath_or_buffer"] del kwds["sep"] kwds_defaults = _refine_defaults_read( dialect, delimiter, engine, sep, on_bad_lines, names, defaults={"delimiter": ","}, dtype_backend=dtype_backend, ) kwds.update(kwds_defaults) return _read(filepath_or_buffer, kwds) @overload def read_table( filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str], *, iterator: Literal[True], chunksize: int | None = ..., **kwds: Unpack[_read_shared[HashableT]], ) -> TextFileReader: ... @overload def read_table( filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str], *, iterator: bool = ..., chunksize: int, **kwds: Unpack[_read_shared[HashableT]], ) -> TextFileReader: ... @overload def read_table( filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str], *, iterator: Literal[False] = ..., chunksize: None = ..., **kwds: Unpack[_read_shared[HashableT]], ) -> DataFrame: ... @overload def read_table( filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str], *, iterator: bool = ..., chunksize: int | None = ..., **kwds: Unpack[_read_shared[HashableT]], ) -> DataFrame | TextFileReader: ... @set_module("pandas") 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[HashableT, 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, skip_blank_lines: bool = True, # Datetime Handling parse_dates: bool | Sequence[Hashable] | None = None, 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 low_memory: bool = _c_parser_defaults["low_memory"], memory_map: bool = False, float_precision: Literal["high", "legacy", "round_trip"] | None = None, storage_options: StorageOptions | None = None, dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default, ) -> DataFrame | TextFileReader: """ Read general delimited file 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 `_. 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 '\\t' (tab-stop) 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. When inferred from the file contents, headers are kept distinct from each other by renaming duplicate names with a numeric suffix of the form ``".{{count}}"`` starting from 1, e.g. ``"foo"`` and ``"foo.1"``. Empty headers are named ``"Unnamed: {{i}}"`` or ``"Unnamed: {{i}}_level_{{level}}"`` in the case of MultiIndex columns. 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. 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. The 'pyarrow' engine is 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. Refers to the number of data rows in the returned DataFrame, excluding: * The header row containing column names. * Rows before the header row, if ``header=1`` or larger. Example usage: * To read the first 999,999 (non-header) rows: ``read_csv(..., nrows=999999)`` * To read rows 1,000,000 through 1,999,999: ``read_csv(..., skiprows=1000000, nrows=999999)`` 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``: empty string, "NaN", "N/A", "NULL", and other common representations of missing data. 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. skip_blank_lines : bool, default True If ``True``, skip over blank lines rather than interpreting as ``NaN`` values. parse_dates : bool, None, list of Hashable, default None The behavior is as follows: * ``bool``. If ``True`` -> try parsing the index. * ``None``. Behaves like ``True`` if ``date_format`` is specified. * ``list`` of ``int`` or names. e.g. If ``[1, 2, 3]`` -> try parsing columns 1, 2, 3 each as a separate date column. 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. date_format : str or dict of column -> format, optional Format to use for parsing dates and/or times when used in conjunction with ``parse_dates``. The strftime to parse time, e.g. :const:`"%d/%m/%Y"`. See `strftime documentation `_ 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 `_ 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 `_ for more information on ``iterator`` and ``chunksize``. compression : str or dict, default 'infer' For on-the-fly decompression of on-disk data. If 'infer' and 'filepath_or_buffer' is path-like, then detect compression from the following extensions: '.gz', '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2' (otherwise no compression). If using 'zip' or 'tar', the ZIP file must contain only one data file to be read in. Set to ``None`` for no decompression. Can also be a dict with key ``'method'`` set to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``, ``'tar'``} and other key-value pairs are forwarded to ``zipfile.ZipFile``, ``gzip.GzipFile``, ``bz2.BZ2File``, ``zstandard.ZstdDecompressor``, ``lzma.LZMAFile`` or ``tarfile.TarFile``, respectively. As an example, the following could be passed for Zstandard decompression using a custom compression dictionary: ``compression={'method': 'zstd', 'dict_data': my_compression_dict}``. 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 `_ . encoding_errors : str, optional, default 'strict' How encoding errors are treated. `List of possible values `_ . 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. - Callable, function that will process a single bad line. - With ``engine='python'``, function with signature ``(bad_line: list[str]) -> list[str] | None``. ``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. - With ``engine='pyarrow'``, function with signature as described in pyarrow documentation: `invalid_row_handler `_. .. versionadded:: 2.2.0 Callable for ``engine='pyarrow'`` 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 : dict, optional Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to ``urllib.request.Request`` as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more details, and for more examples on storage options refer `here `_. dtype_backend : {{'numpy_nullable', 'pyarrow'}} Back-end data type applied to the resultant :class:`DataFrame` (still experimental). If not specified, the default behavior is to not use nullable data types. If specified, the behavior is as follows: * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame` * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype` :class:`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. read_csv : Read a comma-separated values (csv) file into DataFrame. read_fwf : Read a table of fixed-width formatted lines into DataFrame. Examples -------- >>> pd.read_table("data.csv") # doctest: +SKIP Name Value 0 foo 1 1 bar 2 2 #baz 3 Index and header can be specified via the `index_col` and `header` arguments. >>> pd.read_table("data.csv", header=None) # doctest: +SKIP 0 1 0 Name Value 1 foo 1 2 bar 2 3 #baz 3 >>> pd.read_table("data.csv", index_col="Value") # doctest: +SKIP Name Value 1 foo 2 bar 3 #baz Column types are inferred but can be explicitly specified using the dtype argument. >>> pd.read_table("data.csv", dtype={{"Value": float}}) # doctest: +SKIP Name Value 0 foo 1.0 1 bar 2.0 2 #baz 3.0 True, False, and NA values, and thousands separators have defaults, but can be explicitly specified, too. Supply the values you would like as strings or lists of strings! >>> pd.read_table("data.csv", na_values=["foo", "bar"]) # doctest: +SKIP Name Value 0 NaN 1 1 NaN 2 2 #baz 3 Comment lines in the input file can be skipped using the `comment` argument. >>> pd.read_table("data.csv", comment="#") # doctest: +SKIP Name Value 0 foo 1 1 bar 2 By default, columns with dates will be read as ``object`` rather than ``datetime``. >>> df = pd.read_table("tmp.csv") # doctest: +SKIP >>> df # doctest: +SKIP col 1 col 2 col 3 0 10 10/04/2018 Sun 15 Jan 2023 1 20 15/04/2018 Fri 12 May 2023 >>> df.dtypes # doctest: +SKIP col 1 int64 col 2 object col 3 object dtype: object Specific columns can be parsed as dates by using the `parse_dates` and `date_format` arguments. >>> df = pd.read_table( ... "tmp.csv", ... parse_dates=[1, 2], ... date_format={{"col 2": "%d/%m/%Y", "col 3": "%a %d %b %Y"}}, ... ) # doctest: +SKIP >>> df.dtypes # doctest: +SKIP col 1 int64 col 2 datetime64[ns] col 3 datetime64[ns] dtype: object """ # locals() should never be modified kwds = locals().copy() del kwds["filepath_or_buffer"] del kwds["sep"] kwds_defaults = _refine_defaults_read( dialect, delimiter, 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 = ..., iterator: Literal[True], chunksize: int | None = ..., **kwds: Unpack[_read_shared[HashableT]], ) -> 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 = ..., iterator: bool = ..., chunksize: int, **kwds: Unpack[_read_shared[HashableT]], ) -> 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 = ..., iterator: Literal[False] = ..., chunksize: None = ..., **kwds: Unpack[_read_shared[HashableT]], ) -> DataFrame: ... @set_module("pandas") 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, iterator: bool = False, chunksize: int | None = None, **kwds: Unpack[_read_shared[HashableT]], ) -> 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 `_. 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`. 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. **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 and names is not lib.no_default: 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: # for mypy: handled in the if-branch assert index_col is not lib.no_default 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") check_dtype_backend(kwds.setdefault("dtype_backend", lib.no_default)) return _read( filepath_or_buffer, kwds | { "colspecs": colspecs, "infer_nrows": infer_nrows, "engine": "python-fwf", "iterator": iterator, "chunksize": chunksize, }, ) 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 {argname!r} option is not supported with the '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 {argname!r} option is not supported with the " f"{engine!r} 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." ) if hasattr(f, "encoding"): file_encoding = f.encoding orig_reader_enc = self.orig_options.get("encoding", None) any_none = file_encoding is None or orig_reader_enc is None if file_encoding != orig_reader_enc and not any_none: file_path = getattr(f, "name", None) raise ValueError( f"The specified reader encoding {orig_reader_enc} is different " f"from the encoding {file_encoding} of file {file_path}." ) 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"] if sep is not None and len(sep) > 1: if engine == "c" and sep == r"\s+": # delim_whitespace passed on to pandas._libs.parsers.TextReader 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 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 {arg!r} 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 = 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=False, ) 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 if size is None: size = self.nrows - self._currow else: 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 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] = _stringify_na_values(v, floatify) 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) -> set[float]: # 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) -> set[str | float]: """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, 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. 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. """ # 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 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, str | csv.Dialect]) -> 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 isinstance(dialect, str) and dialect in csv.list_dialects(): # get_dialect is typed to return a `_csv.Dialect` for some reason in typeshed tdialect = cast(csv.Dialect, csv.get_dialect(dialect)) _validate_dialect(tdialect) else: _validate_dialect(dialect) tdialect = cast(csv.Dialect, dialect) return tdialect MANDATORY_DIALECT_ATTRS = ( "delimiter", "doublequote", "escapechar", "skipinitialspace", "quotechar", "quoting", ) def _validate_dialect(dialect: csv.Dialect | str) -> 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'")