def read_html(io, match='.+', flavor=None, header=None, index_col=None,
              skiprows=None, attrs=None, parse_dates=False,
              tupleize_cols=False, thousands=',', encoding=None):
    r"""Read HTML tables into a ``list`` of ``DataFrame`` objects.

    Parameters
    ----------
    io : str or file-like
        A URL, a file-like object, or a raw string containing HTML. Note that
        lxml only accepts the http, ftp and file url protocols. If you have a
        URL that starts with ``'https'`` you might try removing the ``'s'``.

    match : str or compiled regular expression, optional
        The set of tables containing text matching this regex or string will be
        returned. Unless the HTML is extremely simple you will probably need to
        pass a non-empty string here. Defaults to '.+' (match any non-empty
        string). The default value will return all tables contained on a page.
        This value is converted to a regular expression so that there is
        consistent behavior between Beautiful Soup and lxml.

    flavor : str or None, container of strings
        The parsing engine to use. 'bs4' and 'html5lib' are synonymous with
        each other, they are both there for backwards compatibility. The
        default of ``None`` tries to use ``lxml`` to parse and if that fails it
        falls back on ``bs4`` + ``html5lib``.

    header : int or list-like or None, optional
        The row (or list of rows for a :class:`~pandas.MultiIndex`) to use to
        make the columns headers.

    index_col : int or list-like or None, optional
        The column (or list of columns) to use to create the index.

    skiprows : int or list-like or slice or None, optional
        0-based. Number of rows to skip after parsing the column integer. If a
        sequence of integers or a slice is given, will skip the rows indexed by
        that sequence.  Note that a single element sequence means 'skip the nth
        row' whereas an integer means 'skip n rows'.

    attrs : dict or None, optional
        This is a dictionary of attributes that you can pass to use to identify
        the table in the HTML. These are not checked for validity before being
        passed to lxml or Beautiful Soup. However, these attributes must be
        valid HTML table attributes to work correctly. For example, ::

            attrs = {'id': 'table'}

        is a valid attribute dictionary because the 'id' HTML tag attribute is
        a valid HTML attribute for *any* HTML tag as per `this document
        <http://www.w3.org/TR/html-markup/global-attributes.html>`__. ::

            attrs = {'asdf': 'table'}

        is *not* a valid attribute dictionary because 'asdf' is not a valid
        HTML attribute even if it is a valid XML attribute.  Valid HTML 4.01
        table attributes can be found `here
        <http://www.w3.org/TR/REC-html40/struct/tables.html#h-11.2>`__. A
        working draft of the HTML 5 spec can be found `here
        <http://www.w3.org/TR/html-markup/table.html>`__. It contains the
        latest information on table attributes for the modern web.

    parse_dates : bool, optional
        See :func:`~pandas.read_csv` for more details.

    tupleize_cols : bool, optional
        If ``False`` try to parse multiple header rows into a
        :class:`~pandas.MultiIndex`, otherwise return raw tuples. Defaults to
        ``False``.

    thousands : str, optional
        Separator to use to parse thousands. Defaults to ``','``.

    encoding : str or None, optional
        The encoding used to decode the web page. Defaults to ``None``.``None``
        preserves the previous encoding behavior, which depends on the
        underlying parser library (e.g., the parser library will try to use
        the encoding provided by the document).

    Returns
    -------
    dfs : list of DataFrames

    Notes
    -----
    Before using this function you should read the :ref:`gotchas about the
    HTML parsing libraries <html-gotchas>`.

    Expect to do some cleanup after you call this function. For example, you
    might need to manually assign column names if the column names are
    converted to NaN when you pass the `header=0` argument. We try to assume as
    little as possible about the structure of the table and push the
    idiosyncrasies of the HTML contained in the table to the user.

    This function searches for ``<table>`` elements and only for ``<tr>``
    and ``<th>`` rows and ``<td>`` elements within each ``<tr>`` or ``<th>``
    element in the table. ``<td>`` stands for "table data".

    Similar to :func:`~pandas.read_csv` the `header` argument is applied
    **after** `skiprows` is applied.

    This function will *always* return a list of :class:`DataFrame` *or*
    it will fail, e.g., it will *not* return an empty list.

    Examples
    --------
    See the :ref:`read_html documentation in the IO section of the docs
    <io.read_html>` for some examples of reading in HTML tables.

    See Also
    --------
    pandas.read_csv
    """
    _importers()

    # Type check here. We don't want to parse only to fail because of an
    # invalid value of an integer skiprows.
    if isinstance(skiprows, numbers.Integral) and skiprows < 0:
        raise ValueError('cannot skip rows starting from the end of the '
                         'data (you passed a negative value)')
    _validate_header_arg(header)
    return _parse(flavor, io, match, header, index_col, skiprows,
                  parse_dates, tupleize_cols, thousands, attrs, encoding)
dataset/ETHPy150Open pydata/pandas/pandas/io/html.py/read_html/original