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# pandas.read_table

pandas.read_table(filepath_or_buffer, *, sep=<no_default>, delimiter=None, header='infer', names=<no_default>, index_col=None, usecols=None, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=0, nrows=None, na_values=None, keep_default_na=True, na_filter=True, skip_blank_lines=True, parse_dates=None, date_format=None, dayfirst=False, cache_dates=True, iterator=False, chunksize=None, compression='infer', thousands=None, decimal='.', lineterminator=None, quotechar='"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, encoding_errors='strict', dialect=None, on_bad_lines='error', low_memory=True, memory_map=False, float_precision=None, storage_options=None, dtype_backend=<no_default>)

  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.

      Certain URL schemes may require additional packages. For example, S3 URLs require the `s3fs` library. See Optional dependencies for a full list.

      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 that accepts an optional size argument, 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. `sep=None` detects the separator from the first valid row of the file with Python’s builtin sniffer tool, `csv.Sniffer`; it is supported only by the Python parsing engine, which will be used automatically. In addition, separators longer than 1 character and different from `'\s+'` will be interpreted as regular expressions and will 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 index or indices in the file to use as DataFrame column labels. Indexing starts at 0 and counts only non-blank, non-commented lines when `skip_blank_lines=True`, so `header=0` denotes the first line of data rather than the first line of the file. Valid arguments are:

      - `int`: line index at which column labels are read.

      - Sequence of `int`: line indices at which column labels are read and combined into a `MultiIndex` on the columns, e.g. `[0, 1, 3]`. Intervening rows that are not specified will be skipped (e.g. row 2 in this example).

      - `None`: no row in the file is interpreted as column labels. Columns are labelled by integer position, or by the values passed to `names` when provided. Use this for files with no header row. If the file has a header row that should be overridden by `names`, pass `header=0` instead.

      - `'infer'` (default): equivalent to `header=0` when no `names` are passed, otherwise equivalent to `header=None`.

      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, `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 `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`. Specifying a `dtype` does not change how missing values are parsed; values recognized as missing (see `na_values` and `keep_default_na`) are still read as `NaN` (or the dtype’s NA value) rather than cast to the requested `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. The pyarrow engine is multithreaded, and the C engine reads sufficiently large files in parallel. 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. The function is applied to the raw text read from the file, before any missing-value detection: an empty field is passed as an empty string `''`, and `na_values` and `keep_default_na` have no effect on a column that has a converter.

    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 `to_datetime()` after `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. `"%d/%m/%Y"`. See strftime documentation for more information on choices, though note that `"%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.

      Added in version 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, 1, 2, 3}, default csv.QUOTE_MINIMAL

      Control field quoting behavior per `csv.QUOTE_*` constants. Use one of `0` or `csv.QUOTE_MINIMAL`, `1` or `csv.QUOTE_ALL`, `2` or `csv.QUOTE_NONNUMERIC`, or `3` or `csv.QUOTE_NONE`. 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.

      Added in version 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. Because each chunk is type-inferred independently, the same literal can be parsed as an `int` in one chunk and a `str` in another; after concatenation the column has `object` dtype and contains both representations, so comparisons such as `df["col"] == 12345` or `df["col"] == "12345"` will match only a subset of the rows holding that value. A `DtypeWarning` is emitted when this occurs. 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 `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.

      Deprecated since version 3.1.0: All float precision modes now use the same converter. The `float_precision` argument will be removed in a future version.

    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 `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 `DataFrame`

      - `"pyarrow"`: returns pyarrow-backed nullable `ArrowDtype` `DataFrame`

      Added in version 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")
         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)
            0      1
      0  Name  Value
      1   foo      1
      2   bar      2
      3  #baz      3

      >>> pd.read_table("data.csv", index_col="Value")
             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})
         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"])
         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="#")
        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")

      >>> df
         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
      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"},
      ... )

      >>> df.dtypes
      col 1             int64
      col 2    datetime64[ns]
      col 3    datetime64[ns]
      dtype: object